Cardiovascular diseases (CVDs) represent a significant global health concern, necessitating effective early detection methods. The advent of wearable electrocardiogram (ECG) devices offers the potential for enhanced portability and continuous monitoring. However, the substantial volume of data generated underscores the need for automated analysis techniques. This paper presents a framework for intra-subject clustering of ECG heartbeats, employing a robust P-QRS-T wave classifier based on a Multiscale Convolutional Neural Network (Multiscale CNN) combined with a Bidirectional Gated Recurrent Unit (biGRU). This is followed by heartbeat segmentation, feature engineering using both manual descriptors and the Time Series Feature Extraction Library (TSFEL), and subsequent clustering via a k-means algorithm. Furthermore, a novel ECG database, the Hi Database (HiDB), is introduced. It was acquired using the Hi 3-Leads ECG (Hi-ECG), a wearable three-lead device commercialised by CGM in collaboration with STMicroelectronics. The proposed wave classifier demonstrated strong performance, tested according to the ANSI/AAMI EC57 standard. It achieved an average sensitivity of 97.79 % and precision of 96 % for QRS detection across the MIT-BIH Arrhythmia Database (MITDB), the American Heart Association ECG Database (AHADB), and the MIT-BIH Noise Stress Test Database (NSTDB). Intra-subject clustering on the MITDB yielded a mean Adjusted Rand Index (ARI) of$0.78 \pm 0.19$and a mean Silhouette score of$0.65 \pm 0.14$. Application of the clustering approach to the HiDB resulted in an average Silhouette score of$0.74 \pm 0.13$. The findings suggest that the presented framework can support clinicians in ECG beat annotation tasks. Its modular design enables adaptability to additional objectives, such as rhythm anomaly detection, by leveraging QRS information from the wave classifier. Both the wave classifier and the feature-based clustering model demonstrated the robustness of the approach across different ECG data sources. Moreover, the intra-subject setting highlights its potential for personalised cardiac monitoring.

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established treatment for motor impairment due to Parkinson's disease (PD) progression. While treated subjects mostly experience significant amelioration of symptoms, some still report adverse effects. In particular, changes in gait patterns due to the electrical stimulation have shown mixed results across studies, with overall gait velocity improvement described as the core positive outcome. This retrospective study investigates changes in the gait parameters of 50 PD patients before and 6 months after STN-DBS, by exploiting a purely data-driven approach. First, unsupervised learning identifies clusters of subjects with similar variations in the gait parameters after STN-DBS. This analysis highlights two dominant clusters (Silhouette score: 0.45, Dunn index: 0.18), with one of them associated to a worsening in walking. Then, supervised machine learning models (i.e., Support Vector Machine and Ensemble Boosting models) are trained using pre-surgery gait parameters, clinical scores, and demographic information to predict the two gait change clusters. In a Leave-One-Subject-Out validation, the best model achieves balanced accuracy 80.05 $\pm$ 3.52 %, denoting moderate predictability of both clusters. Moreover, feature importance analysis reveals the variability in the step width and in the step length asymmetry during the preoperative gait test as promising biomarkers to predict gait response to STN-DBS.

BACKGROUND: Parkinson's Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease progression. Advancements in hand tracking using Deep Learning (DL) allow for the automatic and objective disease evaluation from video recordings of standardised motor tasks, which are the foundation of neurological examinations. In view of this scenario, this narrative review aims to describe the state of the art and the future perspective of DL frameworks for hand tracking in video-based PD assessment. METHODS: A rigorous search of PubMed, Web of Science, IEEE Explorer, and Scopus until October 2023 using primary keywords such as parkinson, hand tracking, and deep learning was performed to select eligible by focusing on video-based PD assessment through DL-driven hand tracking frameworks RESULTS:: After accurate screening, 23 publications met the selection criteria. These studies used various solutions, from well-established pose estimation frameworks, like OpenPose and MediaPipe, to custom deep architectures designed to accurately track hand and finger movements and extract relevant disease features. Estimated hand tracking data were then used to differentiate PD patients from healthy individuals, characterise symptoms such as tremors and bradykinesia, or regress the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) by automatically assessing clinical tasks such as finger tapping, hand movements, and pronation-supination. CONCLUSIONS: DL-driven hand tracking holds promise for PD assessment, offering precise, objective measurements for early diagnosis and monitoring, especially in a telemedicine scenario. However, to ensure clinical acceptance, standardisation and validation are crucial. Future research should prioritise large open datasets, rigorous validation on patients, and the investigation of new frontiers such as tracking hand-hand and hand-object interactions for daily-life tasks assessment.

Freezing of gait is a complex and disabling symptom of Parkinson’s disease, which has a significant impact on the patients’ quality of life and increases the risk of falls and related injuries. This study aims to evaluate the generalization capability of deep learning algorithms in freezing of gait detection. To address this task, various machine learning and deep learning algorithms were implemented, fine-tuned, and evaluated using diverse data splitting and validation strategies. The experiments performed yielded mixed results. Although the implementations demonstrated competitive performance in single-dataset settings (area under the curve ranging from 0.77 to 0.94), all approaches showed limited robustness in cross-dataset tests and suboptimal generalization across different datasets (area under the curve ranging from 0.65 to 0.84). These results highlight the importance of standardized data collection procedures to ensure uniformity. The specification of sensor settings and predefined sensor locations can foster homogeneity in datasets, even when dealing with diverse subjects and environments. Such standardization efforts are crucial for advancing generalized methodologies in the detection of freezing of gait, applicable to both research and clinical applications.

Parkinson's disease (PD) is characterised by a progressive worsening of motor functionalities. In particular, limited hand dexterity strongly correlates with PD diagnosis and staging. Objective detection of alterations in hand motor skills would allow, for example, prompt identification of the disease, its symptoms and the definition of adequate medical treatments. Among the clinical assessment tasks to diagnose and stage PD from hand impairment, the Finger Tapping (FT) task is a well-established tool. This preliminary study exploits a single RGB-Depth camera (Azure Kinect) and Google MediaPipe Hands to track and assess the Finger Tapping task. The system includes several stages. First, hand movements are tracked from FT video recordings and used to extract a series of clinically-relevant features. Then, the most significant features are selected and used to train and test several Machine Learning (ML) models, to distinguish subjects with PD from healthy controls. To test the proposed system, 35 PD subjects and 60 healthy volunteers were recruited. The best-performing ML model achieved a 94.4% Accuracy and 98.4% Fl score in a Leave-One-Subject-Out validation. Moreover, different clusters with respect to spatial and temporal variability in the FT trials among PD subjects were identified. This result suggests the possibility of exploiting the proposed system to perform an even finer identification of subgroups among the PD population.

Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and non-motor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment.

Freezing of Gait (FOG) is one of the most troublesome motor symptoms associated with Parkinson's disease (PD), characterised by brief episodes of inability to step. It involves increased risk of falls and reduced quality of life, and correlates with motor fluctuations and progression of the disease. Hence, the knowledge of FOG event frequency, duration, daily distribution and response to drug therapy is fundamental for a reliable patient's assessment. In this study, we propose a FOG detection algorithm that takes as input inertial data from a single waistmounted smartphone, and provides information about presence and duration of FOG episodes. Data acquisition was carried on 38 PD patients and 21 elderly subjects executing a standard 6-minute walking test. More than 3.5 hours of acceleration data have been collected. A combination of Support Vector Machine and k-Nearest Neighbour classifiers has been designed. Sensitivity of 95.4%, specificity of 98.8%, precision of 92.8% and accuracy of 98.3% in the 10-fold cross validation, and a detection rate of 84% in Leave-one-Subject-Out validation were obtained. These results, along with a good time resolution in the FOG duration identification and very efficient processing times, make the algorithm a promising tool for reliable FOG assessment during activities of daily living.

In this paper we explore detection and tracking of astral micro-tubules, a sub-population of microtubules which only exists during and immediately before mitosis and aids in the spindle orientation by connecting it to the cell cortex. Its analysis can be useful to determine the presence of certain diseases, such as brain pathologies and cancer. The proposed algorithm focuses on overcoming the problems regarding fluorescence microscopy images and microtubule behaviour by using various image processing techniques and is then compared with three existing algorithms, tested on consistent sets of images.

In this paper, we devise a procedure that mimics the behavior of a progressive video stream starting from a non progressive one such as H.264/AVC encoded video. This allows one to unequally protect the video data in an efficient way, according to their importance and the network state. The reported results demonstrate the superior performance of the proposed approach in comparison to state-of-the-art methods for resilient transmission of H.264/AVC data. Moreover, the flexibility in terms of redundancy insertion and achieved quality levels, allows one to span different applications, possibly including P2P video streaming.

We propose a novel standard-compliant multiple description coding (MDC) method that exploits the H.264/AVC redundant slice tool, performing rate-distortion optimization at the slice level. The strategy to allocate redundancy to each slice jointly takes into account its contribution to distortion, its position in the GOP, the effect of decoder error concealment, and the transmission conditions. This makes the algorithm more accurate with respect to previous frame-based solutions, and experimental results show that it compares favorably with other state-of-the-art standard-compliant MDC techniques.

We address distributed source coding with decoder side information, when the decoder observes the source through a noisy channel. Existing approaches employ syndromeor parity-based channel codes. We propose a new approach based on distributed arithmetic coding (DAC).We introduce a DAC with forbidden symbol, which allows to tune the redundancy according to the amount of channel noise. We propose a novel sequential decoder that employs the known side information to decode the corrupted codeword. Experimental results show that the proposed scheme is better than parity-based turbo codes at relatively short block lengths.

This paper provides a summary and overview of the ACM workshop on advanced video streaming techniques for peer-to-peer networks and social networking.

In this letter, we present a performance comparison between multiple description coding (MDC) and unequal loss protection (ULP) for progressive image transmission over lossy packet networks. Two optimization criteria are considered, i.e., a multi-quality criterion, whenNdistinct quality levels are guaranteed at the decoder side, and the optimization of the expected quality at the receiver. We resort to both a semi-analytical approach and simulation results. To enable numerical comparisons, we address a specific MDC algorithm suitable for progressive imaging, and a state-of-the-art ULP algorithm based on Reed Solomon codes. The results, although cannot be generalized to any MDC and ULP methods, are useful to put into evidence some general features that can drive the selection of the most proper technique for the application at hand. In fact, they allow to put into evidence the main advantages and drawbacks of either technique.

Digital fountain codes have emerged as a low-complexity alternative to Reed-Solomon codes for erasure correction. The applications of these codes are relevant especially in the field of wireless video, where low encoding and decoding complexity is crucial. In this paper, we introduce a new class of digital fountain codes based on a sliding-window approach applied to Raptor codes. These codes have several properties useful for video applications, and provide better performance than classical digital fountains. Then, we propose an application of sliding-window Raptor codes to wireless video broadcasting using scalable video coding. The rates of the base and enhancement layers, as well as the number of coded packets generated for each layer, are optimized so as to yield the best possible expected quality at the receiver side, and providing unequal loss protection to the different layers according to their importance. The proposed system has been validated in a UMTS broadcast scenario, showing that it improves the end-to-end quality, and is robust towards fluctuations in the packet loss rate.

Multiple description coding (MDC) makes use of redundant representations of multimedia data to achieve resiliency. Descriptions should be generated so that the quality obtained when decoding a subset of them only depends on their number and not on the particular received subset. In this paper, we propose a method based on the principle of encoding the source at several rates, and properly blending the data encoded at different rates to generate the descriptions. The aim is to achieve efficient redundancy exploitation, and easy adaptation to different network scenarios by means of fine tuning of the encoder parameters. We apply this principle to both JPEG 2000 images and H.264/AVC video data. We consider as the reference scenario the distribution of contents on application-layer overlays with multiple-tree topology. The experimental results reveal that our method favorably compares with state-of-art MDC techniques.

SEACAST is a peer-to-peer live streaming protocol developed at Politecnico di Torino, which aims at improving current systems in two key areas. The first is the use of fullfledged flow control using RTP/UDP and session signaling. The second is the use of multiple description coding to handle error resilience and user heterogeneity. In this paper we overview SEACAST, highlighting its main innovations, and providing a short summary of performance evaluation over a local testbed at Politecnico di Torino. The results show a definite performance improvement with respect to existing systems, and point out the usefulness of multiple description coding in the peer-to-peer context.

We propose a distributed source coding system for data collected by sensor networks. It uses a feedback channel between the sensors and the gateway node (i.e., the joint decoder) but, unlike previous systems, the encoding process is driven by the decoder. Compression is performed using distributed arithmetic coding, which is extended to adaptively estimate the source probabilities. Specifically, the decoder estimates marginal and conditional probabilities, and sends them back to the sensors to drive the distributed arithmetic coding process. This reduces the decoding delay, and potentially eliminates the need of rate-compatible Slepian-Wolf codes.

In this paper we address a novel scheme to protect video sequences according to slice importance based on slice reordering, ULP and error-concealment techniques. The approach does not require the modification to the video decoder although an application-layer channel coding is required. Simulation results show that the proposed algorithm outperforms state-of-the-art approaches, reducing the gap with the upper-bound error-free performance curve. Moreover, the complexity of the additional stage required to pilot the protection allocation stage is negligible with respect to traditional ULP schemes.

In this letter, we propose a novel unequal loss protection scheme, which allocates FEC codes to video slices according to their impact on the GOP distortion. This is evaluated taking the concealment procedure and the drift effect into account. Simulation results show that the proposed algorithm outperforms state-of-the-art approaches, reducing the gap with the error-free performance curve. Moreover, the complexity of the additional stage required to pilot the protection allocation stage is negligible with respect to traditional ULP schemes.

We propose an algorithm that improves the performance of rate-distortion-based multiple description coding (RD-MDC). The gain is particularly significant in the high redundancy region, where RD-MDC suffers a major performance penalty with respect to MDC bounds. The improvement is obtained with negligible additional computational cost, by exploiting the coarse information also at the central decoder. The proposed method can be generalized to all MDC schemes that use scalar quantization, without modifying the quantizer structure. This feature guarantees the generation of descriptions that can be decoded without any modification of the decoder.

In this paper, a novel H.264 multiple description technique is proposed. The coding approach is based on the redundant slice representation option, defined in the H.264 standard. In presence of losses, the redundant representation can be used to replace missing portions of the compressed bit stream, thus yielding a certain degree of error resilience. This paper addresses the creation of two balanced descriptions based on the concept of redundant slices, while keeping full compatibility with the H.264 standard syntax and decoding behavior in case of single description reception. When two descriptions are available still a standard H.264 decoder can be used, given a simple preprocessing of the received compressed bit streams. An analytical setup is employed in order to optimally select the amount of redundancy to be inserted in each frame, taking into account both the transmission condition and the video decoder error propagation. Experimental results demonstrate that the proposed technique favorably compares with other H.264 multiple description approaches.

In this paper a novel conditional access scheme for the distribution of H.264/AVC video is presented. The algorithm permits to cypher the full quality video, while guaranteeing free access to a reduced quality video that can be used as preview. The scheme is based on the novel coding options, introduced in the H.264 standard, and therefore is fully compliant with the standard syntax. The secured video stream exhibits a very small rate overhead and requires limited supplementary computational cost at coding time.

In this paper a novel H.264 multiple description technique is proposed. The coding approach is based on the redundant slice representation option, defined in the H.264 standard. In presence of losses, the redundant representation can be used to replace missing portions of the compressed bitstream, thus yielding a certain degree of error resilience. This paper addresses the creation of two balanced descriptions based on the concept of redundant slices, while keeping full compatibility with the H.264 standard syntax and decoding behavior. Moreover, a practical algorithm for redundancy tuning as a function of the packet loss rate is provided. Experimental results demonstrate that the proposed technique favorably compares with other H.264 multiple description approaches.

Digital fountain codes are becoming increasingly important for multimedia communications over networks subject to packet erasures. These codes have significantly lower complexity than Reed-Solomon ones, exhibit high erasure correction performance, and are very well suited to generating multiple equally important descriptions of a source. In this paper we propose an innovative scheme for streaming multimedia contents by using digital fountain codes applied over sliding windows, along with a suitably modified belief-propagation decoder. The use of overlapped windows allows one to have a virtually extended block, which yields superior performance in terms of packet recovery. Simulation results using LT codes show that the proposed algorithm has better performance in terms of efficiency, reliability and memory with respect to fixed-window encoding.

We propose a new scheme for symmetric Slepian-Wolf coding of correlated binary sources. Unlike previous designs that employ capacity-achieving channel codes, the proposed scheme is based on arithmetic codes with error correction capability. We define a time-sharing version of a distributed arithmetic coder, and a soft joint decoder. Experimental results on two sources show that, for short block length, the proposed scheme outperforms the symmetric turbo code design in (Stankovic et al., 2006).

We propose a new lossy compression algorithm for hyperspectral images, which is based on the spectral Karhunen-Loeve transform, followed by spatial JPEG 2000, which employs a model of anomalous pixels during the compression process. Results on Airborne Visible/Infrared Imaging Spectrometer scenes show that the new algorithm provides better rate-distortion performance, as well as improved anomaly detection performance, with respect to the state of the art.

Multiple description coding (MDC) is a good way to combat packet losses in error-prone networks subject to packet erasures. However, redundancy tuning is often difficult, and this makes the generation of descriptions with good redundancy-rate-distortion performance a hard job. Moreover, the complexity of generating more than two descriptions represents a strong limitation to MDC. In this letter, we propose a method that exploits the data rate-distortion characteristics to generate multiple descriptions for JPEG 2000 with tunable redundancy levels. The identification of the best number of descriptions as a function of the network conditions is also addressed

In this letter, the mismatch error due to the replacement of a fine with a coarse quantizer is considered, and an analytical model is proposed to describe the related distortion. Simulations show that this model is highly accurate and can be used to estimate the expected distortion of DPCM-based codecs in order to better allocating the rate. For highly correlated sources, this leads to a gain of 1 to 1.5 dB over an exhaustive search method that adopts a uniform redundancy allocation. Moreover, it permits to allocate the redundancy by an easy-to-solve analytical model of the system.

Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen–Loève transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications.

In this paper, an innovative joint-source channel coding scheme is presented. The proposed approach enables iterative soft decoding of arithmetic codes by means of a soft-in soft- out decoder based on suboptimal search and pruning of a binary tree. An error-resilient arithmetic coder with a forbidden symbol is used in order to improve the performance of the joint source/channel scheme. The performance in the case of transmission across the AWGN channel is evaluated in terms of word error probability and compared to a traditional separated approach. The interleaver gain, the convergence property of the system, and the optimal source/channel rate allocation are investigated. Finally, the practical relevance of the proposed joint decoding approach is demonstrated within the JPEG 2000 coding standard. In particular, an iterative channel and JPEG 2000 decoder is designed and tested in the case of image transmission across the AWGN channel.

In this paper, a novel multiple description coding technique is proposed, based on optimal Lagrangian rate allocation. The method assumes the coded data consists of independently coded blocks. Initially, all the blocks are coded at two different rates. Then blocks are split into two subsets with similar rate distortion characteristics; two balanced descriptions are generated by combining code blocks belonging to the two subsets encoded at opposite rates. A theoretical analysis of the approach is carried out, and the optimal rate distortion conditions are worked out. The method is successfully applied to the JPEG 2000 standard and simulation results show a noticeable performance improvement with respect to state-of-the art algorithms. The proposed technique enables easy tuning of the required coding redundancy. Moreover, the generated streams are fully compatible with Part 1 of the standard.

Multiple description coding can be implemented as pre- and postprocessing to all standards for image and video communications, with obvious advantages. This can be achieved by generating two subsets from the original data; a controllable amount of extra redundancy between the descriptions has to be inserted to help the estimation of the possibly lost description from the received one. This redundancy can be in the form of spatial oversampling. In this paper, we propose and develop a mathematical framework for two descriptions pre- and postprocessing methods, which exploit the correlation characteristics of the visual data in order to better implement the multiple description coding paradigm. Simulation results show a noticeable performance improvement of both the proposed methods with respect to state-of-the art algorithms, in terms of both rate/redundancy/distortion tradeoff and computational complexity.

In this paper we address the problem of providing conditional access to video sequences, namely, to generate a low-quality video to be used as preview, which can be decoded at full quality if a decryption key is obtained. We propose and investigate the performance of two different techniques, based on smoothing and separate encoding in the compressed domain, and motion vector perturbation. We show that these techniques are able to provide conditional access to different quality levels of H.264/AVC video with very small rate overhead, and that their combination can provide different levels of security towards malicious attacks

In order to store, and retrieve images from large databases, we propose a framework, based on multiple description coding paradigms, that disseminates images over distributed servers. Consequently, decentralized download can be performed, thus reducing links overload and hotspot areas without penalizing downloads speed. Moreover, the tradeoff between system reliability and storage requirement can be achieved by tuning descriptions redundancy, thus providing high flexibility in terms of storage resources, reliability of access, and performance. The scalability of the proposed framework is achieved by the intrinsic progressivity of the multiple description schemes. Moreover, we demonstrate that system can work properly regardless of server crashes

In this letter we propose a new technique for progressive coding of hyperspectral data. Specifically, we employ a hybrid three-dimensional wavelet transform for spectral and spatial decorrelation in the framework of Part 2 of the JPEG 2000 standard. Both onboard and on-the-ground compression are addressed. The resulting technique is compliant with the JPEG 2000 family of standards and provides competitive performance with respect to state-of-the-art techniques.

JPEG 2000 is the novel ISO standard for image and video coding. Besides its improved coding efficiency, it also provides a few error resilience tools in order to limit the effect of errors in the codestream, which can occur when the compressed image or video data are transmitted over an error-prone channel, as typically occurs in wireless communication scenarios. However, for very harsh channels, these tools often do not provide an adequate degree of error protection. In this paper, we propose a novel error-resilience tool for JPEG 2000, based on the concept of ternary arithmetic coders employing a forbidden symbol. Such coders introduce a controlled degree of redundancy during the encoding process, which can be exploited at the decoder side in order to detect and correct errors. We propose a maximum likelihood and a maximum a posteriori context-based decoder, specifically tailored to the JPEG 2000 arithmetic coder, which are able to carry out both hard and soft decoding of a corrupted code-stream. The proposed decoder extends the JPEG 2000 capabilities in error-prone scenarios, without violating the standard syntax. Extensive simulations on video sequences show that the proposed decoders largely outperform the standard in terms of PSNR and visual quality.

We propose a novel multimedia security framework based on a modification of the arithmetic coder, which is used by most international image and video coding standards as entropy coding stage. In particular, we introduce a randomized arithmetic coding paradigm, which achieves encryption by inserting some randomization in the arithmetic coding procedure; notably, and unlike previous works on encryption by arithmetic coding, this is done at no expense in terms of coding efficiency. The proposed technique can be applied to any multimedia coder employing arithmetic coding; in this paper we describe an implementation tailored to the JPEG 2000 standard. The proposed approach turns out to be robust towards attempts to estimating the image or discovering the key, and allows very flexible protection procedures at the code-block level, allowing to perform total and selective encryption, as well as conditional access

The novel H.264/AVC video coding specification provides a significant improvement in terms of coding efficiency compared to previous standards. In certain streaming scenarios, the problem of whole-frame loss concealment can arise when using H.264/AVC. Two different algorithms have been proposed to conceal a lost image, based on the optical flow concept. In a real streaming scenario, an encoder usually introduces some kind of intra refresh policy that can insert trouble concealment algorithms leaving gaps in the motion vector field used for reference. We discuss the advantages of introducing a motion vector estimator for such intra coded regions.

Despite multiple description coding (MDC) paradigm is effective to combat packet losses on networks, it is not included in any multimedia co-decoding standard. This can be justified by the complexity of the redundancy tuning task of many of the proposed methods. Moreover, the complexity of generating more than two descriptions represents a strong limitation to MDC. In this paper, we generalize the MDC scheme proposed for JPEG 2000 in T. Tillo and G. Olmo (2004) to more than two descriptions. Using this flexible MDC scheme, we show the importance of properly tuning both the redundancy and the number of descriptions to match the network condition. Finally, we derive the conditions in order to determine when it is better to use single description coding or MDC

In remote sensing systems, on-board data compression is a crucial task that has to be carried out with limited computational resources. In this paper we propose a novel lossless compression scheme for multispectral and hyperspectral images, which combines low encoding complexity and high-performance. The encoder is based on distributed source coding concepts, and employs Slepian-Wolf coding of the bitplanes of the CALIC prediction errors to achieve improved performance. Experimental results on AVIRIS data show that the proposed scheme exhibits performance similar to CALIC, and significantly better than JPEG 2000.

Hyperspectral image compression has recently attracted a remarkable interest for remote sensing applications. In this paper we propose a unified embedded lossy-to-lossless compression framework based on the JPEG 2000 standard. In particular, we exploit the multicomponent transformation feature of Part 2 of JPEG 2000 to devise a compression framework based on a spectral decorrelating transform followed by JPEG 2000 compression of the transformed coefficients. We evaluate several possible choices for the spectral transform, including a floating-point DCT, an integer DCT, and a wavelet transform. The final version of the proposed algorithm has been compared to 3D-SPIHT in the lossy case, and to several state-of-the-art compression algorithms including JPEG-LS and 3D-CALIC in the lossless case. Experimental results on AVIRIS data show that the proposed technique exhibits very competitive performance for both reversible and irreversible compression, with significantly lower complexity than DPCM-based methods, and memory requirements compatible with typical onboard processing subsystems of remote sensing platforms.

In this paper a novel scalable video coder, based on the principle of distributed source coding with side information is proposed. Coding scalability is achieved by means of bitplane coding in the wavelet domain. The distributed coding paradigm is applied to encode the wavelet coefficients of a given frame, by considering those of the previous frame as side information. LDPC syndrome encoding with proper context modeling of the frame correlation allowed us to significantly outperform intra coding obtained with JPEG 2000. Moreover, the proposed approach permits to perform motion compensation at the decoder side, thus opening a new perspective in the field of scalable video coding

When transmitting multimedia information over non-prioritized networks subject to packet losses, multiple description coding with an arbitrary number of descriptions is an effective choice in order to minimize the end-to-end distortion. Such descriptions should be generated so that the quality obtained decoding a subset of them depends only on their number and not on the particular received subset. In this paper, we propose an encoding procedure to generate an arbitrary number of balanced descriptions using a multi-rate allocation scheme which exploits the R-D characteristic of the data

In this paper, a novel maximum a posteriori (MAP) estimation approach is employed for error correction of arithmetic codes with a forbidden symbol. The system is founded on the principle of joint source channel coding, which allows one to unify the arithmetic decoding and error correction tasks into a single process, with superior performance compared to traditional separated techniques. The proposed system improves the performance in terms of error correction with respect to a separated source and channel coding approach based on convolutional codes, with the additional great advantage of allowing complete flexibility in adjusting the coding rate. The proposed MAP decoder is tested in the case of image transmission across the additive white Gaussian noise channel and compared against standard forward error correction techniques in terms of performance and complexity. Both hard and soft decoding are taken into account, and excellent results in terms of packet error rate and decoded image quality are obtained.

In this paper, we propose an efficient method for the allocation of Reed-Solomon codes to source symbols, for unequal loss protection. The proposed formulation recasts the multivariate optimization problem into a univariate one, dramatically reducing the computational complexity. Results are shown for image transmission over lossy packet networks, employing the JPEG2000 and SPIHT encoders. The proposed algorithm exhibits performance equivalent to previous methods, while providing a significant complexity reduction.

In low bit-rate packet-based video communications, video frames may have very small size, so that each frame fills the payload of a single network packet; thus, packet losses correspond to whole-frame losses, to which the existing error concealment algorithms are badly suited and generally not applicable. In this paper, we deal with the problem of concealment of whole frame-losses, and propose a novel technique which is capable of handling this very critical case. The proposed technique presents other two major innovations with respect to the state-of-the-art: i) it is based on optical flow estimation applied to error concealment and ii) it performs multiframe estimation, thus optimally exploiting the multiple reference frame buffer featured by the most modern video coders such as H.263+ and H.264. If data partitioning is employed, by e.g., sending headers, motion vectors, and coding modes in prioritized packets as can be done in the DiffServ network model, the algorithm is capable of exploiting the motion vectors to improve the error concealment results. The algorithm has been embedded in the H.264 test model software, and tested under both independent and correlated packet loss models with parameters typical of the wireless environment. Results show that the proposed algorithm significantly outperforms other techniques by several dBs in peak signal-to-noise ratio (PSNR), provides good visual quality, and has a rather low complexity, which makes it possible to perform real-time operation with reasonable computational resources.

In this paper an innovative joint source channel coding scheme is presented. The system is based on iterative soft decoding of arithmetic codes, by means of a novel soft-in soft-out decoder based on suboptimal search and pruning of a binary tree. An error resilient arithmetic coder with a forbidden symbol is used in order to improve the performance of the joint source/channel scheme. The performance in the case of transmission across the AWGN channel is evaluated in terms of frame error rate, and compared to a traditional separated approach. Finally the convergence property of the system is analyzed by means of the EXIT chart technique.

In this paper a novel error resilient MQ coder for reliable JPEG 2000 image delivery is designed. The proposed coder uses a forbidden symbol in order to force a given amount of redundancy in the codestream. At the decoder side, the presence of the forbidden symbol allows for powerful error correction. Moreover the added redundancy can be easily controlled and the proposed coder is kept backward compatible with MQ. In this work excellent improvements in the case of image transmission across both BSC and AWGN channels are obtained by means of a maximum a posteriori estimation technique.

In the multiple description paradigm, a controllable amount of redundancy is inserted among descriptions, in order to help estimating those ones which are possibly lost due to network congestion. This redundancy can also be exploited in order to correct errors at bit level. In this paper, we propose a novel technique to generate multiple descriptions of video encoded with motion-JPEG 2000, which exploits the inserted extra redundancy also to guarantee error protection in case all descriptions are received, but are possibly affected by bit errors. This method yields excellent performance, since it guarantees not only protection of video information transmitted over non prioritized networks subject to independent packet erasure processes, but also resilience towards the corruption at bit level. Moreover, the generated streams are fully compatible with the part 3 of the JPEG 2000 standard.

A new error resilience tool is proposed for robust JPEG 2000 imaging over noisy channels. In particular, a modified encoder, based on an MQ arithmetic coder with forbidden symbol, is introduced, along with a maximum likelihood error-correcting MQ decoder. The proposed technique features error detection, error concealment and error correction capability, thus adding new useful functionalities to JPEG 2000. Experimental results show that this technique largely outperforms the standard JPEG 2000 error resilience tools for error concealment and hard/soft channel decoding.

Nowadays, wireless multimedia applications are experiencing a rapid growth; in this scenario, challenging obstacles, such as packet losses due to congestion and band limitation along with bit-level error corruption, require the design of novel solutions for robust multimedia delivery. New standards for multimedia applications are incorporating many tools for error resilience; as an example, JPEG 2000 part 11 is explicitly devoted to the wireless applications of the image coding standard. In this paper we address a novel multiple description coding technique, based on post-processing rate allocation of embedded bitstreams. The proposed approach is compliant with the JPEG 2000 standard and has the ability to jointly cope with both packet losses and bit errors. Experimental results show that the designed algorithm significantly outperforms other techniques based on unequal error protection by means of RS codes.

We propose a new lossless and near-lossless compression algorithm for hyperspectral images based on context-based adaptive lossless image coding (CALIC). Specifically, we propose a novel multiband spectral predictor, along with optimized model parameters and optimization thresholds. The resulting algorithm is suitable for compression of data in band-interleaved-by-line format; its performance evaluation on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data shows that it outperforms 3-D-CALIC as well as other state-of-the-art compression algorithms.

We propose a novel technique to generate rate-distortion optimized multiple descriptions of images, exploiting the rate-allocation strategy embedded in the JPEG2000 encoder. The proposed scheme can be applied to any encoding algorithm, given that the rate allocation is based on code-block truncation. The method yields excellent performance in terms of both central and side distortion, outperforming state-of-the art techniques. Moreover, the single description decoding is fully compatible with the JPEG2000 Part 1 decoder.

We present hybrid loss protection as a new channel coding and packetization scheme for image transmission over nonprioritized lossy packet networks. The scheme employs an interleaver-based structure, and attempts to maximize the expected peak signal-to-noise ratio (PSNR) at the receiver given the constraint that the probability of failure, i.e., the probability that the PSNR of the decoded image is below a given threshold, is upper-bounded by a user-defined value. A new code-allocation algorithm is proposed, which employs Gilbert-Elliot modeling of the network statistics. Experimental results are provided in the case of transmission of images encoded by SPIHT and JPEG 2000 over a wireline, as well as a wireless UMTS-based Internet connection.

We propose a spatio-temporal error concealment algorithm for video transmission in an error-prone environment. The proposed technique employs motion vector estimation, edge-preserving interpolation, and texture analysis/synthesis. It has two main advantages with respect to existing methods, namely: (i) it aims at optimizing the visual quality of the restored video, and not only PSNR; and (ii) it employs an automatic mode selection algorithm in order to decide, on a macroblock basis, whether to use the spatial restoration, the temporal one, or a combination thereof. The algorithm has been applied to H.26L video, providing satisfactory performance over a large set of operating conditions.

In this paper, two novel maximum a posteriori (MAP) estimators for the decoding of arithmetic codes in the presence of transmission errors are presented. Trellis search techniques and a forbidden symbol are employed to obtain forward error correction. The proposed system is applied to lossless image compression and transmission across the BSC; the results are compared in terms of both performance and complexity with a traditional separated source and channel coding approach based on convolutional codes.

We propose a reduced complexity technique for the rate-distortion optimization in JVT/H.26L in the presence of packet erasures. It is named "few decoders in the encoder", and is based on the idea of generating a selected number of error patterns in the encoder, so that a limited number of co-decoding processes can be implemented to estimate the transmission distortion term. The correlation amongst packet erasures is taken into account by employing a binary Gilbert model. The proposed algorithm exhibits competitive performance in terms of average PSNR and probability of decoding failure, with very affordable complexity and memory requirements.

A known problem in video streaming is that loss of a packet usually results into loss of a whole video frame. In this paper we propose an error concealment algorithm specifically designed to handle this sort of losses. The technique exploits information in a few past frames (namely the motion vectors) in order to estimate the forward motion vectors of the last received frame. This information is used to project the last frame onto an estimate of the missing frame. The algorithm has been tested on MPEG-2 video, providing very satisfactory results, and outperforming by several dBs in PSNR the concealment technique based on repetition of the last received frame.

We proposed a spatio-temporal error concealment algorithm for video transmission in an error-prone environment. The proposed technique employs motion vector estimation, edge-preserving interpolation, and texture analysis/synthesis. It has two main advantages with respect to existing methods, namely: i) it aims at optimizing the visual quality of the restored video, and not only PSNR; and ii) it employs an automatic mode selection algorithm in order to decide, on a macroblock basis, whether to use the spatial restoration, the temporal one, or a combination thereof. The algorithm has been applied to H26L video, providing satisfactory performance over a large set of operating conditions.

In this paper we study the problem of transmitting coded video over a UMTS network. We first discuss the statistical characteristics of packet losses in case of RTP/UDP/IP wireless video communication. Then, we propose a new rate allocation algorithm for H.264 video, based on a Gilbert-Elliot model of the packet losses. We compare the proposed algorithm with several other allocation strategies, showing that it achieves satisfactory performance in terms of PSNR. Moreover, we outline the limits of the employed distortion model and outline possible solutions to overcome them.

In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. We show that, due to the statistical properties of the SAR signal, an along-range linear predictor with few taps is able to effectively capture most of the raw signal correlation. The proposed predictive coding algorithm, which performs quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off, with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods based on transform-coding; fractional output bit-rates can also be achieved in the entropy-constrained mode. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems.

In this paper we propose a novel error concealment algorithm for video transmission over wireless networks potentially subject to packet erasures. In particular, we develop a technique for the replenishment of missing macroblocks, which aims at minimizing the impact of the lost data on the resulting video with respect to the human visual system. The proposed algorithm operates three reconstruction stages at different scales, by first recovering smooth large-scale patterns, then large-scale structures, and finally local edges in the lost macroblock. Experimental results show that the proposed algorithm achieves improved visual quality of the reconstructed frames with respect to other state-of-the-art techniques, as well as better PSNR results.

The lifting scheme is a very well-known computationally efficient alternative to the filter bank scheme for evaluating the discrete wavelet transform of signals and images. However, the actual computational saving is still a matter of debate. On one hand, theoretical results in the literature report an asymptotic upper-bound of two for very long wavelet filters. On the other hand, it is worth wondering to what extent the architecture of the processor used can actually bias this gain. In this paper we tackle this problem from an implementation perspective, and profile the execution time of the two algorithms on a digital signal processor. Both the real-valued and the integer versions of the wavelet transform are considered. The quantitative results are used to gain some insight on the way the processor architecture affects the algorithms.

In the context of joint source and channel coding, unequal loss protection is often used to make image data more robust to possible packet losses. The allocation of source and code symbols is customarily done so as to maximize the expected PSNR at the receiver. We propose a new objective function, attempting to maximize PSNR given a constraint on the system probability of failure, so that PSNR is constrained to be above a given threshold with a given probability. This leads to the definition of a hybrid loss protection scheme, and the related allocation algorithm, which is able to satisfy this constraint. Experimental results are reported, related to the transmission of JPEG2000-compressed images over the Internet. It is shown that the proposed hybrid approach outperforms existing algorithms in terms of PSNR, while requiring less computational resources.

We propose near-lossless digital watermarking for copyright protection of remote sensing images. In particular, we show that, by forcing a maximum absolute difference between the original and watermarked scene, the near-lossless paradigm makes it possible to decrease the effect of watermarking on remote sensing applications to be carried out on the images. As an example, the effect of near-lossless watermarking on image classification is analyzed.

In this article we evaluate the use of predictive coding for compression of SAR phase history data. We first show that the data are mainly correlated along range lines. Then, we exploit this result to define a new DPCM-based compression algorithm named RDPCM-BAQ. The performance of this algorithm is compared with that of BAQ on SIR-C/X-SAR data, showing a significant improvement in signal-to-noise ratio of up to 2 dB with respect to BAQ.

In this paper we compare two compression methods for SAR raw data, based on the discrete wavelet transform (DWT). In the former, the data are subject to blockwise normalization prior to being transformed by means of the DWT processor; then, an optimal rate allocation is performed for each subband. The latter employs the well known JPEG 2000 to perform the DWT and the subsequent quantization, rate allocation and coding steps; both the cases of normalized and non normalized data are considered. The performance of the algorithms have been tested on SIR-C/X-SAR data, and FBAQ is employed as the term of comparison for both quality and compression ratio. The obtained results show that the if the samples are normalized, the performance of the DWT-based algorithms is more predictable, and less subject to statistical fluctuations related to the characteristics of the input data.

This paper deals with the design and implementation of an image transform coding algorithm based on the integer wavelet transform (IWT). First of all, criteria are proposed for the selection of optimal factorizations of the wavelet filter polyphase matrix to be employed within the lifting scheme. The obtained results lead to the IWT implementations with very satisfactory lossless and lossy compression performance. Then, the effects of finite precision representation of the lifting coefficients on the compression performance are analyzed, showing that, in most cases, a very small number of bits can be employed for the mantissa keeping the performance degradation very limited. Stemming from these results, a VLSI architecture is proposed for the IWT implementation, capable of achieving very high frame rates with moderate gate complexity.

The Hough transform is a widely used tool for line detection mainly due to its robustness to noise; on the other hand, it is also known to be computationally expensive, thus often preventing real-time operation. In this paper we evaluate the performance of an all-integer version of the Hough transform, implemented without floating point operations. We show that the integer transform is 2 to 3.5 times faster than the standard one on most platforms, while its performance loss is negligible.

Carrier frequency estimation plays an important role in digital communication receivers. A progressive refinement approach to maximum-likelihood estimation is proposed as a means to improve the performance of a suboptimal estimation algorithm and achieve different tradeoffs among complexity, accuracy and dynamic range. As an example, an application is described for carrier frequency recovery in burst-mode digital transmissions. The Luise and Reggiannini (1995) synchronization algorithm is employed as the master frequency estimator. The performance of the algorithm is demonstrated by means of a simulation study, assuming quadrature phase-shift keying modulation; the results show that the progressive refinement approach can be usefully exploited, for example to improve the estimation range of the LR algorithm, without losing the accuracy and the computational complexity.

We propose an on-board selection scheme for aerial and space images, based on linear feature detection in a feature hyperspace. The detection task is performed by means of the Radon transform (RT) and the wavelet transform; a fast algorithm for the RT computation is described, and counteractions against the discretization errors are proposed. A new, wavelet-based algorithm is introduced, which performs a fine analysis of the waveforms of the RT peaks, yielding a possibly error-free detection in images corrupted by a high level of noise. A technique, based on the feature hyperspace, is proposed, able to significantly exploit all the available pieces of information on these peaks. Results of the tests on synthetic and real images are reported, which show that this method achieves satisfactory results, making the detection task highly reliable in the presence of both noise and clutter.

In this paper we deal with the problem of finding high performance factorizations of wavelet filters to be employed within the lifting scheme framework to yield the integer wavelet transform (IWT). A method is proposed, based on the search for the factorization yielding the minimally non-linear iterated graphic function. Results are reported, referring to a set of popular wavelet filters, which show that the obtained implementations lead to IWTs achieving very satisfactory results for both lossy and lossless image compression.

This paper investigates the robustness of the wavelet transform, implemented by means of the lifting scheme (LS), with respect to numerical errors in the representation and calculation of transformed coefficients. The study promises to offer important contributions for the understanding of the LS capabilities when specific implementations are considered. This is a topic of growing interest as the new standard JPEG 2000, based on the wavelet transform, is being finalized. Taking into account the effect of finite precision representation can drive both software and hardware implementations with optimized trade off between complexity and performance; moreover the robustness to numerical errors could be an important feature, not usually considered, in order to select the best wavelet filters.

In many Earth observation missions, a large amount of data are collected by the on-board sensors, and must be transmitted to ground through a channel with limited capacity; in this case, besides lossy compression, one often has to select a subset of the original images for ground transmission. It is then desirable that the discarded images are those with a minor information content. In this paper we propose an algorithm for on-board image selection, which is fully integrated within a wavelet-based image compression scheme. The algorithm selects images possibly containing straight patterns, and uses the Hough transform, performed in the wavelet domain, for the detection task. It is shown that this method achieves a notable complexity reduction at the expense of a limited performance degradation, strongly increasing the sustainable real-time data throughput.

Turbo estimation algorithms (TEAs) for non-random parameters are able to yield high accuracy estimates by means of an iterative process. At each iteration, a noise reduction is performed by means of an external denoising system (EDS), which exploits the estimation results obtained at the previous step; the enhanced data are then input to the master estimation algorithm (EA) for next iteration. A basic TEA scheme has been previously proposed in the context of modal analysis, which makes use of the Tufts and Kumaresan (1982) algorithm as the master EA, and of a multiband IIR filter as the EDS. In this paper, two improvements of this basic scheme are proposed; the former implies a different design of the EDS, able to achieve better estimation accuracy while reducing the outlier probability; the latter permits the autodetermination of the number of modes making up the signal.

The application of the well-known technique of trellis coded modulation to coherent optical communications using polarization shift keying (POLSK) is described and analyzed. The resulting receiver is formed by a front-end which performs the heterodyne detection and the Stokes parameter extraction, cascaded with an electronic Viterbi processor operating the maximum likelihood estimate of the transmitted sequence. Results in terms of the error event probability using optimum as well as a simpler suboptimum branch metric show power gains of the order of 3-4 dB, at the expense of a reasonable increase in complexity, only concerning the processing in the electronic domain. These coding gains are not lost even in the presence of high levels of phase noise, to which POLSK in general is highly insensitive.>

The authors present the results of a design study of the receiver in a digital transmission system using the combined coding and modulation schemes known as Ungerboeck codes. Specifically, they examine the design of the receiver for encoded 16-PSK (phase shift keying) modulation, presenting first the traditional structure for the optimum receiver and then a simpler structure. The decoding depth of the Viterbi algorithm, the quantization of the metrics inside the Viterbi processor, and the phase jitter in the recovered carrier are considered. The impact of branch and path metric quantization inside the receiver is discussed, showing that a reasonable number of bits (8) is sufficient to obtain nearly optimum performance when the code complexity is limited. The effect of imperfect carrier recovery inside the receiver is studied, providing accurate analytical estimates of the error event probability as well as an upper bound to the symbol error probability. Results of a detailed simulation, including carrier and bit timing recovery blocks, show that the effects of imperfections on the bit error probability are very small, even at low signal-to-noise ratios. On the whole, results show the robustness of the Viterbi algorithm with respect to fairly rough quantizations of the metrics and indicate that carrier recovery is not as critical as expected.>