Abstract Litter pollution remains a pressing environmental issue, motivating the search for automated monitoring methods that can scale effectively. The problem is heightened by the difficulty of detecting small and diverse litter objects across wide areas, prompting interest in Unmanned Aerial Vehicles (UAVs) and deep learning as viable solutions. We conducted a systematic literature review by initially using Google Scholar, and then iteratively expanding our search through bibliographic references to identify relevant studies and datasets. In this review, we: synthesize the applicability of nine publicly available litter datasets; compile and analyze computer vision integrations with Litter Management Systems, with a particular emphasis towards UAV-based solutions; and review relevant literature addressing this issue; among others. Our analysis includes four UAV-based datasets (BDW, UAVVaste, HAIDA, SODA) and five non-UAV datasets (TrashNet, TACO, MJU-Waste, PlastOPol, ZeroWaste), examining dataset characteristics, preprocessing techniques, model architectures, and evaluation metrics across studies. Our synthesis of the literature highlights the varied approaches different studies undertake, reflecting the complexity of the task and the absence of standardised protocols. We conclude by discussing priorities for future work, notably the need for more publicly available in-the-wild UAV-acquired datasets and the potential of newer model architectures to address current limitations in automated litter detection.

Neural Image Compression (NIC) has achieved superior compression performance by modeling images as implicit feature representations, yet its practical deployment is severely hindered by computational overhead. Recently, GaussianImage was proposed as a computationally efficient explicit image representation paradigm, which renders images from 2D Gaussians. However, it suffers from inferior compression performance relative to mainstream NIC frameworks, mainly due to low compressibility and representation capability of explicit Gaussian parameters. To this end, we propose Pixel-Aligned Generalized 2D Splatting (PA-G2DS) as a computation-efficient and compression-friendly image representation format. Specifically, we deploy a learnable rendering function with implicit coefficients to enhance the reconstructed image quality and improve the compression ratio over explicit Gaussian coefficients. Incorporating the proposed PA-G2DS as a computationally efficient decoder, we further develop a suite of image codecs optimized for either compression ratios or flexible deployment scenarios. Experiments prove that the proposed codecs could achieve 30ms compression latency and millisecond-level decompression latency, reducing the performance and efficiency gap between implicit and explicit image representation. Furthermore, the proposed codec opens potential applications for NIC such as JPEG-like sequential decompression and random-access during decompression.

Learned lossless image compression has been a popular topic in recent research. While outperforming many traditional compression techniques in terms of compression ratio, the high time and space complexity of such methods prevent them from wider applications. Recently, Finite-State AutoRegressive (FSAR) entropy coding was proposed, boosting autoregressive entropy coding for the quantized latent space utilizing a lookup table. However, such a method still consumes an extraordinary amount of memory, which prevents its application on low-end devices. In this work, we propose the Distribution Quantized FSAR (DQ-FSAR) model, building a two-step lookup table for the FSAR model that significantly reduces memory occupation. Furthermore, we design compact loss functions to optimize the DQ-FSAR model. Experiments show that our method reduces more than 99% memory usage compared to the baseline, with a similar compression ratio and speed.

Modern Unmanned Aerial Vehicles (UAVs) are equipped with high-resolution cameras that can capture imagery and video of whatever lies in their field of view. However, depending on the altitude of the UAV, objects within the imagery appear small, sometimes covering just a few pixels. This makes the detection of such objects very challenging. The SODA (Small Objects at Different Altitudes) dataset was created to assist in research dealing with small object detection from aerial imagery. The dataset contains 377 aerial images captured using UAVs at altitudes ranging between 5m and 30 m. The dataset is then used to evaluate the performance of YOLOv8. The SODA dataset is publicly available on GitHub1.

In modern computer vision models, the quality and quantity of training data have become crucial. Datasets deemed sufficient a few years ago now require data augmentation to increase their size. This presents a challenge, especially when these supplementary datasets lack annotations in standard formats like COCO, VGG, or YOLO. One solution to this problem is to learn semantic boundaries from binary images of unannotated datasets, thereby increasing the data available for training and evaluating models. However, choosing an efficient annotation method can be both time-consuming and effort-intensive. This research paper explores three approaches, ranging from traditional image processing algorithms to the recently introduced Segment Anything Model (SAM). The study demonstrates how these different algorithms perform on various datasets and concludes that the proposed image processing method strikes the best balance between performance and efficiency.

It is attractive to deploy free-viewpoint video (FVV) systems based on the multi-view video plus depth (MVD) representation in crowd event scenarios, as it provides viewers a new, interactive and immersive multimedia. Nevertheless, such scenarios are notoriously challenging for mobile network operators in view of the relatively high uplink user data and signaling overhead traffic. The latter is caused by channel quality indicator (CQI) feedback reports sent by the user equipment (UEs) and used by the eNB for the downlink scheduling functionality. This paper extends our previous work on CQI feedback reduction through a clustering technique, and proposes an enhanced content-aware M-LWDF based packet scheduling scheme coupled with a cross-layer CQI feedback clustering adaptation scheme. The simulation results show that the proposed solution reduces uplink feedback signaling by 74.9%, when compared to the 3GPP wideband scheme, whilst improving the visual quality of both the texture and synthesized views.

This paper presents a cross-layer depth-texture target bit rate allocation estimation technique for the transmission of Multiview High Efficiency Video Coding (MV-HEVC) texture plus depth content over 3GPP LTE systems. The proposed technique is based on a statistical model which exploits the texture and depth map image characteristics to estimate the optimal depth-texture rate allocation to be used by the codec's rate control algorithm. Experiments using standard test sequences show the effectiveness of the proposed technique as the model is able to estimate, on-line, the optimal depth-texture rate allocation with a mean absolute estimation error of 3.3% and a standard deviation of 2.2%. In addition, the proposed cross-layer architecture allows the depth-texture rate allocation to be adapted to both the video content characteristics and the available bandwidth offered by the wireless network making it suitable for the transmission of multiview 3D video to mobile devices.

Crowd events such as public demonstrations, sports events and mass entertainment gatherings pose additional challenges to mobile network operators due to the asymmetric traffic profile. In such crowd events, networks are typically uplink limited whilst also requiring a significant amount of signaling information to be sent by the user equipment (UE) to the evolved Node B (eNB). This signaling includes channel quality indicator (CQI) feedback reports which provide necessary information for the selection of the downlink transmission parameters such as the modulation and coding scheme. In this work, the authors address this crowd event signaling problem by considering a common sports event scenario, whereby users are seated in a football stadium, and propose a novel CQI clustering scheme to reduce the feedback report signaling. The proposed scheme was implemented and tested in an LTE system level simulator, which was purposely modified to model the scenario, and has shown efficacy with an overall CQI feedback signaling reduction of up to 91% whilst maintaining stable sector throughput, when compared to the standard third generation partnership project (3GPP) CQI feedback mechanism.

The third generation partnership project (3GPP) long‐term evolution (LTE) cellular system offers high data rate capabilities by leveraging several techniques including link adaptation and frequency selective scheduling. These techniques rely on accurate channel quality indicator (CQI) feedback reports that are sent by the user equipment (UE) to the evolved node B, a process which results in high signalling overhead. In this study, the authors propose a UE‐assisted sub‐band feedback compression scheme based on a predictive filtering technique to reduce this signalling overhead. Four schemes based on adaptive filters have been designed, implemented and tested in an LTE system level simulator. Simulation results indicate that the proposed compression scheme has shown efficacy with an overall CQI feedback signalling reduction of up to 92.5% whilst maintaining stable sector throughput, when compared with the standard 3GPP CQI feedback mechanism. Although the proposed scheme exhibits low computational and memory complexity, a reduced‐complexity scheme achieving an average computational load reduction of up to 35% is also presented.

Object selection is a challenge in computer vision since it is generally a trade-off between accuracy and performance. A popular approach is the use of bounding boxes around objects that are to be selected. Other common techniques provide a set of objects from which the user can then choose. The method presented in this paper is designed around the priority of performance and granular selection of objects in the scene. Experiments performed on a non-parallel implementation of the proposed solution return results in an average time of 0.043s. The technique also returned very good results in the processing of objects that are partially occluded, hence enabling future work in improved identification and recognition of such objects.

This paper presents the development of a fast Free-Viewpoint Video (FW) rendering algorithm that exploits the parallelism offered by General Purpose Graphics Processing Units (GPGPUs). The system generates virtual views through the use of Depth Image-Based Rendering (DIBR) algorithms, implemented using NVidia® Compute Unified Device Architecture (CUDA). A novel reference image brightness adjustment algorithm that exploits the correspondences between matching pixels in the reference images to avoid drastic brightness switching while navigating in between views is also discussed. The developed solution ensures that data transfers are kept at a minimum, thus improving the overall rendering speed. Objective and subjective test results show that, for typical free-view scenarios, the proposed algorithm can be successfully deployed in real-time FW systems, providing a good Quality of Experience (QoE).

With the availability and rapid advancement of low-cost, low-power, and high-performance processors, machine vision is gaining popularity in various fields, including that of autonomous navigation systems. Applying feature extraction techniques on the captured images provides rich information about the surrounding environment that can be used to accurately determine the position, velocity, and orientation of a vehicle. To extract these features in such an application, we developed the Single Octave Scale Invariant Feature Transform (Single Octave SIFT). This solution drastically reduces the computational load and memory bandwidth requirements while providing an accurate image-based terrain referenced navigation system for micro- and small-sized Unmanned Aerial Vehicles (UAVs). The Gaussian filtering and Keypoint extraction stages are the most computationally intensive parts of the Single Octave SIFT. The main focus of this paper is the design of this modified SIFT algorithm and the basic building blocks needed to implement these two stages within a low-cost, low-power and small footprint Xilinx Spartan-6 LX150 FPGA. Simulation results show that the number of memory accesses is reduced by 99.7% for Full-HD (1920×1080) images1. The operation cycles of the Gaussian filtering and Keypoint extraction stages are reduced by 90.2% and 95% respectively, compared with the single instruction multiple data (SIMD) architecture.

Distributed video coding (DVC) is a paradigm that can shift most of the computational intensive tasks from the encoder to the decoder. This allows for the design of low complexity encoders that can be deployed in devices equipped with limited resources. However, the compression efficiency obtained using practical DVC codecs are still distant from those of traditional predictive video coding schemes such as H.264/AVC. One of the limitations of the existing DVC architectures is that they consider the correlation noise to be randomly distributed across the whole video frame. This paper shows that the Syner–Ziv (WZ) values that are closer to the endpoints of the quantization intervals have a higher probability of producing incorrect side information (SI) predictions. Thus, through this knowledge, rate-adaptive low-density parity-check accumulate codes that provide a higher level of protection to the unreliable SI bits can be exploited. Experimental results show that the proposed scheme can reduce the WZ bit-rate up to 13% relative to the state-of-the-art DISCOVER architecture when considering interpolation techniques and by up to 0.9 dB for extrapolation techniques.

Despite theorems predicting that Distributed Video Coding can achieve the same performance as traditional predictive video coding schemes, the coding efficiency of practical architectures is still far from these bounds. This is attributed to the poor Side Information (SI) estimated at the decoder and to the inability of the channel codes to recover the source at the Slepian-Wolf (SW) limits. This paper tackles the latter issue by recovering the SI bit-planes starting from the most unreliable bit of each coefficient. Most of the mismatch in SI is thus accumulated within the first decoded bit-planes, leaving the last bit-planes with very few or no mismatch. Low-Density Parity-Check Accumulate (LDPCA) codes can then benefit from such compact distribution of correlation noise since they offer a smaller percentage error, from the SW bounds, when mismatch is accumulated in few higher entropy bit-planes. Furthermore, with this setup, most of the last bit-planes can be recovered very effectively using just 8-bit or 16-bit Cyclic Redundancy Codes. Experimental results show that the proposed scheme can reduce the Wyner-Ziv bit-rates by up to 21% compared to the DISCOVER codec.

The characteristics of the depth map video are different from those of the texture and thus the empirical function of the Lagrange multiplier λ normally used in the rate-distortion optimization (RDO) of texture views might not be suitable for depth map coding. In this paper, we propose a technique whereby the Lagrange multiplier used to select the macroblock (MB) mode is adapted based on the discontinuity region of the depth map and the frame type. This technique, which was tested on various standard test sequences, preserves the depth discontinuities leading to an average depth map bit rate saving of 12.5% without incurring significant quality degradation in the synthesized views compared to using the fixed Lagrange multiplier used in the respective multi-view texture videos.

This paper presents an adaptive texture-depth target bit rate allocation estimation technique for low latency multi-view video plus depth transmission using a multi-regression model. The proposed technique employs the prediction mode distribution of the macroblocks at the discontinuity regions of the depth map video to estimate the optimal texture-depth target bit rate allocation considering the total available bit rate. This technique was tested using various standard test sequences and has shown efficacy as the model is able to estimate, in real-time, the optimal texture-depth rate allocation with an absolute mean estimation error of 2.5% and a standard deviation of 2.2%. Moreover, it allows the texture-depth rate allocation to be adapted to the video sequence with good tracking performance, allowing the correct handling of scene changes.

The Distributed Video Coding (DVC) paradigm can theoretically reach the same coding efficiencies of predictive block-based video coding schemes, like H.264/AVC. However, current DVC architectures are still far from this ideal performance. This is mainly attributed to inaccuracies in the Side Information (SI) predicted at the decoder. The work in this paper presents a coding scheme which tries to avoid mismatch in the SI predictions caused by small variations in light intensity. Using the appropriate rounding operator for every coefficient, the proposed method significantly reduces the correlation noise between the Wyner-Ziv (WZ) frame and the corresponding SI, achieving higher coding efficiencies. Experimental results demonstrate that the average Peak Signal-to-Noise Ratio (PSNR) is improved by up to 0.56dB relative to the DISCOVER codec.

This paper presents a novel view-level target bit rate distribution estimation technique for real-time Multi-view video plus depth using a statistical model that is based on the prediction mode distribution. Experiments using various standard test sequences show the efficacy of the technique, as the model manages to estimate online the view-level target bit rate distribution with an absolute mean estimation error of 2% and a standard deviation of 0.9%. Moreover, this technique provides adaptation of the view-level bit rate distribution providing scene change handling capability.

Motion and disparity estimations are employed in Multi-view Video Coding (MVC) to remove redundancies present between temporal and different viewpoint frames, respectively, in both the color and the depth multi-view videos. These constitute the major computational expensive tasks of the video encoder, as iterative search for the optimal mode and its appropriate compensation vectors is employed to reduce the Rate-Distortion Optimization (RDO) cost function. This paper proposes a solution to limit the number of modes that are tested for RDO to encode the inter-view predicted views. The decision is based on the encoded information obtained from the corresponding Macroblock in the Base view, identified accurately by using the multi-view geometry together with the depth data. Results show that this geometric technique manages to reduce about 70% of the estimation's computational time and can also be used with fast geometric estimations to reduce up to 95% of the original encoding time. These gains are obtained with little degradation on the multi-view video quality for both color and depth MVC.

The persistent improvements witnessed in multimedia production have considerably augmented users demand for immersive 3D systems. Expedient implementation of this technology however, entails the need for significant reduction in the amount of information required for representation. Depth image-based rendering algorithms have considerably reduced the number of images necessary for 3D scene reconstruction, nevertheless the compression of depth maps still poses several challenges due to the peculiar nature of the data. To this end, this paper proposes a novel depth representation methodology that exploits the intrinsic correlation present between colour intensity and depth images of a natural scene. A segmentation-based approach is implemented which decreases the amount of information necessary for transmission by a factor of 24 with respect to conventional JPEG algorithms whilst maintaining a quasi identical reconstruction quality of the 3D views.

The application of advanced error concealment techniques applied as a post-process to conceal lost video information in error-prone channels, such as the wireless channel, demand additional processing at the receiver. This increases the delivery delay and needs more computational power. However, in general, only a small region within medical video is of interest to the physician and thus if only this area is considered, the number of computations can be curtailed. In this paper we present a technique whereby the Region of Interest (ROI) specified by the physician is used to delimit the area where the more complex concealment techniques are applied. A cross layer design approach in mobile WiMAX wireless communication environment is adopted in this paper to provide an optimized Quality of Experience (QoE) in the region that matters most to the mobile physician while relaxing the requirements in the background, ensuring real-time delivery. Results show that a diagnostically acceptable Peak Signal-to-Noise-Ratio (PSNR) of about 36 dB can still be achieved within reasonable decoding time.

Distributed Video Coding (DVC) is a coding paradigm where video statistics are exploited, partially or totally, at the decoder. The performance of such a codec depends on the accuracy of the soft-input information estimated at the decoder, which is affected by the quality of the side information (SI) and the dependency model. This paper studies the discrepancies between the bit planes of the Wyner-Ziv (WZ) frames and the corresponding bit planes of the SI. The relationship between these discrepancies is then exploited to predict the locations where the bit plane of the SI is expected to differ from that of the original WZ frame. This information is then used to derive more accurate soft-input values that achieve better compression efficiencies. Simulation results demonstrate that a WZ bit-rate reduction of 9.4% is achieved for a given video quality.

The Multi-view Video Coding (MVC) technique provides significantly better coding efficiency compared to simulcast transmission of different camera view-points. This is done by exploiting both motion and disparity compensation techniques to compress the different view-point videos. This paper proposes a more efficient MVC technique that transmits the optimally selected compensation replacements with respect to more accurate compensation vector predictors obtained using the multi-view geometry and the depth information. In this technique, the SKIP mode is also modified to adapt its compensation direction from both the temporal and the view-point reference frames. Experimental results show that the proposed MVC technique gives an average gain in video quality of about 0.5dB, for the interview predicted view-points.

Disparity estimation is used in Multi-view Video Coding (MVC) to remove the inter-view redundancies present in both color and depth multi-view video sequences. The standard H.264/MVC achieves high compression efficiency by deriving the optimal disparity vector through the exhaustive calculation of the Rate-Distortion cost function for all the possible search points. This makes disparity estimation highly computational expensive. This paper proposes an efficient technique that exploits both the multi-view and the epipolar geometries to determine the optimal search area, resulting in a reduction of search points and thus computations. Simulation results show that this technique can save up to 95% of the computational cost for disparity estimation, with negligible loss in coding efficiency for both the color and the depth multi-view video coding.

Distributed Video Coding (DVC) is a coding paradigm which shifts the major computational intensive tasks from the encoder to the decoder. Temporal correlation is exploited at the decoder by predicting the Wyner-Ziv (WZ) frames from the adjacent key frames. Compression is then achieved by transmitting just the parity information required to correct the predicted frame and recover the original frame. This paper proposes an algorithm which identifies most of the unreliable bits in the predicted bit planes, by considering the discrepancies in the previously decoded bit plane. The design of the used Low Density Parity Check (LDPC) codes is then biased to provide better protection to the unreliable bits. Simulation results show that, for the same target quality, the proposed scheme can reduce the WZ bit rates by up to 7% compared to traditional schemes.

Real-time wireless conversational and broadcasting multimedia applications offer particular transmission challenges as reliable content delivery cannot be guaranteed. The undelivered and erroneous content causes significant degradation in quality of experience. The H.264/AVC standard includes several error resilient tools to mitigate this effect on video quality. However, the methods implemented by the standard are based on a packet-loss scenario, where corrupted slices are dropped and the lost information concealed. Partially damaged slices still contain valuable information that can be used to enhance the quality of the recovered video. This study presents a novel error recovery solution that relies on a joint source-channel decoder to recover only feasible slices. A major advantage of this decoder-based strategy is that it grants additional robustness while keeping the same transmission data rate. Simulation results show that the proposed approach manages to completely recover 30.79% of the corrupted slices. This provides frame-by-frame peak signal-to-noise ratio (PSNR) gains of up to 18.1 dB, a result which, to the knowledge of the authors, is superior to all other joint source-channel decoding methods found in literature. Furthermore, this error resilient strategy can be combined with other error resilient tools adopted by the standard to enhance their performance.

Multi-view video coding exploits inter-view redundancies to compress the video streams and their associated depth information. These techniques utilize disparity estimation techniques to obtain disparity vectors (DVs) across different views. However, these methods contribute to the majority of the computational power needed for multi-view video encoding. This paper proposes a solution for fast disparity estimation based on multi-view geometry and depth information. A DV predictor is first calculated followed by an iterative or a fast search estimation process which finds the optimal DV in the search area dictated by the predictor. Simulation results demonstrate that this predictor is reliable enough to determine the area of the optimal DVs to allow a smaller search range. Furthermore, results show that the proposed approach achieves a speedup of 2.5 while still preserving the original rate-distortion performance.

Wireless infrastructures have seen a drastic increase in energy requirements as technology shifted towards higher frequencies in an attempt to increase bandwidth. This is further accentuated by increasingly demanding real-time services such as video streaming. To cater for these new services and still guarantee an adequate quality of service, wireless network operators in large cities have to install more base stations and access points, hence increasing power consumption. Mobile peer networking is a possible solution for power-efficient wireless communication, since it reduces infrastructural requirements and peer transmission over short distances demands less power. A novel algorithm based on multicast protocols, traffic sampling, and state switching is proposed. Results show that this technique presents a remarkable power efficiency improvement over standard peer-to-peer networks.

This letter presents a hybrid error control and artifact detection (HECAD) mechanism which can be used to enhance the error resilient capabilities of the standard H.264/advanced video coding (AVC) codec. The proposed solution first exploits the residual source redundancy to recover the most likelihood H.264/AVC bitstream. If error recovery is unsuccessful, the residual corrupted slices are then passed through a pixel-level artifact detection mechanism to detect the visually impaired macroblocks to be concealed. The proposed HECAD algorithm achieves overall peak signal-to-noise ratio gains between 0.4 dB and 4.5 dB relative to the standard with no additional bandwidth requirement. The cost of this solution translates in a marginal increase in the complexity of the decoder. In addition, this method can be applied in conjunction with other error resilient strategies and scales well with different encoding configurations.

Solutions that provide practical mobile peer networking have to date been ineffective for a number of reasons; these include challenges to address ubiquitous radio access networks, cost per bandwidth, as well as the spatial-temporal availability of each peer node within the mobile network. A new peer networking algorithm involving peer nodal hierarchies, traffic mapping and neural networks is proposed. Results show that the technique presents a remarkable network performance improvement over standard peer-to-peer networks. Comparisons with alternative peer topologies also show that the proposed solution exhibits higher performance in peer applications, suggesting better usage of the limitations present in mobile networks.

The H.264/AVC standard employs a number of error-resilient mechanisms to correct transmission errors. These methods assume a packet-loss scenario, where all the macroblocks (MBs) contained within a corrupted slice are dropped and concealed. However, most of the MBs contained within corrupted slices provide minimal (if any) visual distortions and therefore concealing them causes a superfluous drop in the quality of the recovered video content. This paper presents a novel error control mechanism which employs prior information and residual source-redundancy to recover the most-likelihood feasible H.264/AVC bitstream. Simulation results show that the algorithm recovers a number of corrupted sequences and achieves overall peak signal-to-noise ratio (PSNR) gains between 1 dB and 2 dB over the standard. The proposed solution is compatible with the H.264/AVC with no additional bandwidth requirements.

Exploitation of mobile peer networking in providing data and multimedia services has to-date been limited for a number of reasons, these include difficulties in addressing different ubiquitous radio access networks, cost per bandwidth, as well as increased TCP congestion due to reduced radio resources. A new mobile peer networking algorithm involving adaptive window techniques based on Linear-Quadratic-Gaussian (LQG) control is proposed. Results show that the proposed technique presents a remarkable network performance improvement over standard algorithms.

Wireless sensor networks are generally deployed in remote areas where no infrastructure is available. This imposes the use of battery operated devices which seriously limits the lifetime of the network. In this paper we present a cluster-based routing algorithm which is based on Fuzzy-ART neural networks to maximize the life span of such networks. Results show that the energy saving obtained improves the network lifetime by 79.6%, 17.1% and 22.4% (in terms of First Node Dies) when compared to LEACH, a centralised version of LEACH and a self-organizing map (SOM) neural network-based clustering algorithm respectively. Furthermore, this paper explores the use of a base station centric predictive filtering algorithm to reduce the amount of transmitted data leading to a further increase in network lifetime.

Ka-band satellite multimedia communication networks play important roles because of their capability to provide the required bandwidth in remote places of the globe. However, because of design complexity, in practice they suffer from poor design and performance degradation because of being practically forced to guarantee acceptable end-user satisfaction in conditions of extremely low bit error rates, which is emphasised with the vulnerability of compressed video content to transmission errors, often impossible to be applied during the service development phase. A novel discrete event simulation model is presented, which provides performance estimation for such systems based on subjective measurement and a better quality of experience. The authors show that the proposed model reduces implementation cost and is flexible to be used for different network topologies around the globe.

Wireless multimedia services are increasingly becoming popular boosting the need for better quality-of-experience (QoE) with minimal costs. The standard codecs employed by these systems remove spatio-temporal redundancies to minimize the bandwidth required. However, this increases the exposure of the system to transmission errors, thus presenting a significant degradation in perceptual quality of the reconstructed video sequences. A number of mechanisms were investigated in the past to make these codecs more robust against transmission errors. Nevertheless, these techniques achieved little success, forcing the transmission to be held at lower bit-error rates (BERs) to guarantee acceptable quality. This paper presents a novel solution to this problem based on the error detection capabilities of the transport protocols to identify potentially corrupted group-of-blocks (GOBs). The algorithm uses a support vector machine (SVM) at its core to localize visually impaired macroblocks (MBs) that require concealment within these GOBs. Hence, this method drastically reduces the region to be concealed compared to state-of-the-art error resilient strategies which assume a packet loss scenario. Testing on a standard H.263++ codec confirms that a significant gain in quality is achieved with error detection rates of 97.8% and peak signal-to-noise ratio (PSNR) gains of up to 5.33 dB. Moreover, most of the undetected errors provide minimal visual artifacts and are thus of little influence to the perceived quality of the reconstructed sequences.

The transmission of H.264/AVC encoded sequences over noisy wireless channels generally adopt the error detection capabilities of the transport protocol to identify and discard corrupted slices. All the macroblocks (MBs) within each corrupted slice are then concealed. This paper presents an algorithm that does not discard the corrupted slices but tries to detect those MBs which provide major visual artefacts and then conceal only these MBs. Results show that the proposed solution, based on a set of image-level features and two support vector machines (SVMs), manages to detect 94.6% of those artefacts. Gains in peak signal-to-noise ratios (PSNR) of up to 5.74 dB have been obtained when compared to the standard H.264/AVC decoder.

Current trends in wireless communications provide fast and location-independent access to multimedia services. Due to its high compression efficiency, H.264/AVC is expected to become the dominant underlying technology in the delivery of future wireless video applications. The error resilient mechanisms adopted by this standard alleviate the problem of spatio-temporal propagation of visual artifacts caused by transmission errors by dropping and concealing all macroblocks (MBs) contained within corrupted segments, including uncorrupted MBs. Concealing these uncorrupted MBs generally causes a reduction in quality of the reconstructed video sequence.

Forward error correction (FEC schemes are generally used in wireless communication systems to maintain an acceptable quality of service. Various models have been proposed in literature to predict the end-to-end quality of wireless video systems. However, most of these models utilize simplistic error generators which do not accurately represent any practical wireless channel. A more accurate way is to evaluate the quality of a video system using Monte Carlo techniques. However these necessitate huge computational times, making these methods unpractical. This paper proposes an alternative method that can be used in modeling of complex communications systems with minimal computational time. The proposed three random variable method was used to model two FEC schemes adopted by the digital video broadcasting (DVB) standard. Simulation results confirm that this method closely matches the performance of the considered communication systems in both bit error rate (BER) and peak signal-to-noise ratio (PSNR).

Novel low-voltage, low-power techniques in the design of portable wireless communication systems are required. Two system examples of low-power analog multipliers operating from a 1.2 V supply are presented. These proposed structures achieve the required multiplication function by using current processing. The circuits were fabricated using standard double-poly CMOS processes for a 900 MHz application. Measurement results of the prototypes are comparable to other higher voltage designs.

A low-voltage analog multiplier operating at 1.2V is presented. The multiplier core consists of four MOS transistors operating in the saturation region. The circuit exploits the quadratic relation between current and voltage of the MOS transistor in saturation. The circuit was designed using standard 0.6μm CMOS technology. Simulation results indicate an IP3 of 4.9dBm and a spur free dynamic range of 45dB.