In this study, multi‐user (MU) underwater acoustic communication is investigated using massive multiple‐input multiple‐output (MIMO) orthogonal frequency division multiplexing (OFDM)‐based orthogonal time frequency space modulation (OTFS) systems. The performance of a 4‐user scenario is evaluated over a simulated 1 km vertically‐configured time‐varying underwater acoustic channel (UAC) in terms of bit error rate and maximum achievable bit rate. Considering 64‐QAM and frequency‐domain pilot‐based channel estimation, it is shown that the underwater vehicles employing the OFDM‐based OTFS modulation outperform those using the conventional OFDM modulation in a dynamic UAC. The application of massive MIMO allows the four underwater vehicles to use the same time and frequency resources to transmit their information reliably to a surface station. Furthermore, it is shown that each underwater vehicle in the MU‐massive MIMO transmission scenario can achieve an effective bit rate as high as 198.7 kbps over the 1 km UAC using four transmitting transducers.

Developing an objective video quality metric that accurately estimates perceived video quality is challenging. Developing a metric that can additionally be embedded in the rate distortion optimization process of a video codec can be even harder given that decisions have to be made locally. In this paper, we present a method for combining a number of existing state of the art objective video quality metrics at the coding block level by employing a fusion of local content features for deciding how to best utilize the chosen metrics. Our results indicate promising performance in terms of the correlation of the developed locally-acting quality metric with the overall perceived quality of the video.

When watching a video, the viewer's eyes will fixate on a certain point within each frame. Areas far from the viewers gaze location are perceived with much lower visual acuity than those around the fixation point. This effect is known as foveation. In this paper, the effect of foveation on High Dynamic Range (HDR) video perception is investigated. Using eye tracking data recorded from six different HDR sequences, the bit depth of individual frames are variably encoded, with the pixels with the highest bit depth corresponding to areas around the most likely fixation point for the frame. The bit depth of pixels within the modified frame will then gradually reduce, dependent on how far the pixel is located from the fixation point. To lower the bit depth of the HDR content, a tone mapping operator (TMO) is used. The particular TMO that is used generates an optimal tone mapping curve for every frame, which is used for both tone mapping to reduce the bit depth, and for inverse tone mapping for display purposes. However, this procedure can often cause large amounts of flickering, as well as banding artefacts, which reduce the perceptual quality of the video. Methods to mitigate these effects are proposed and implemented in this paper. Subjective performance evaluations were carried out involving 17 participants in order to evaluate the proposed methodology. Results show that when the lowest bit depth is 8 bits, the modified video is indistinguishable from the original. However, when 6 bit regions are introduced, a significant difference is noticed. Dithering and increasing the foveation region significantly improves the perceptual quality of the modified sequence.

In this paper we investigate the application of the non-orthogonal multiple access (NOMA) technique for multiuser underwater acoustic (UWA) communication. The NOMA scheme can be implemented using either orthogonal frequency division multiplexing (OFDM) or filterbank multicarrier (FBMC) modulation for waveform shaping. In order to boost the throughput over a 1 km time-varying underwater acoustic channel (UAC), spatially multiplexed multiple-input multiple-output (MIMO) systems are considered. We evaluate the bit error rate (BER), packet error rate (PER) and maximum bit rate performances of Turbo-coded NOMA-OFDM and NOMA-FBMC systems for a 2-user scenario where both users utilize the same frequency bandwidth. It is shown that while both the NOMA-OFDM and NOMA-FBMC systems show comparable performance in terms of BER and PER, the MIMO NOMA-FBMC system however achieves higher bit rates than the OFDM-based system.

The present paper considers the application of massive multiple-input multiple-output (MIMO) to a multi-user (MU) underwater acoustic (UWA) communication system. In the scope to achieve a high throughput within a limited acoustic bandwidth, we consider filterbank multicarrier (FBMC) modulation for waveform shaping instead of the widely used orthogonal frequency division multiplexing (OFDM). We assess the performance of the system in a 1 km simulated time-varying underwater acoustic channel (UAC) in terms of bit error rate (BER), packet error rate (PER) and maximum achievable throughput. The transmission scenario consists of four users, each one equipped with multiple transmitting hydrophones. We show that the implementation of massive MIMO at the receiver allows all the users to use the same frequency bandwidth to transmit their information reliably to the receiver. Therefore, a high spectral efficiency and throughput can be achieved, making the transmission of multimedia data such as high quality real-time video a reality.

In this paper we propose to use massive multiple-input multiple-output (MIMO) systems in order to boost the throughput over a 1000 m vertically-configured time-varying underwater acoustic channel (UAC). We compare the bit error rate (BER) performance of un-coded and Turbo-coded massive MIMO systems based on filter bank multicarrier (FBMC) modulation and Orthogonal Frequency Division Multiplexing (OFDM). A modified FBMC system is considered whereby complex data symbols are transmitted instead of real-valued ones. This is made possible by spreading the data symbols in time while still ensuring maximum bandwidth efficiency and low complexity. It is shown that the coded FBMC systems outperform the coded OFDM systems both in terms of error performance and bit rate for similar massive MIMO configuration. Video transmission is evaluated over the UAC with the systems that achieve the highest bit rates and it is shown that real-time transmission is possible with acceptable video quality over the long range acoustic link.

In this paper, a novel spatial resolution adaptation approach for video compression is proposed. Its ability to dynamically apply downsampling to frames exhibiting low spatial detail delivers improved rate distortion performance, together with a reduction in computational complexity of the encoding process. This method is based on an experimental investigation of the dependence between the QP threshold, which determines when to encode lower resolution frames, and the distortion obtained after downsampling/upsampling. The proposed approach is integrated with the High Efficiency Video Coding (HEVC) reference codec for intra coding, and evaluated on 15 high-resolution test sequences with varying levels of spatial detail. The results show a promising average bitrate savings of approximately 4% (B-D measurements), and significant complexity reduction (29% on average).

In this paper we propose a No-Reference Image Quality Assessment (NR-IQA) method on High Dynamic Range (HDR) images by combining deep Convolutional Neural Networks (CNNs) with saliency maps. The proposed method utilises the power of deep CNN architectures to extract quality features which can be applied cross HDR and Standard Dynamic Range (SDR) domains. To introduce human visual system to CNNs, a saliency map algorithm is used to select a subset of salient image patches to evaluate on. Our CNN-based method delivers a state-of-the-art performance in HDR NR-IQA experiment, competitive with full reference IQA methods.

This paper introduces a method for adapting block quantisation parameter values in HEVC video compression based on perceptual coding maps. These maps are computed per block taking into account masking effects. Masking levels are calculated using spatial, temporal and foveation features that are extracted from the video and are stored in a perceptual coding map. The produced map drives a QP adaptation process that aims to redistribute coding bits in the frame so that the perceived quality is improved, especially at those bitrates where coding artifacts become visible (mid to high QP values). The subjective performance evaluation that was conducted showed that the proposed method can offer a measurable improvement in perceived quality relative to a constant QP approach, with Bjontegaard mean opinion scores (MOS) gains reaching almost 9% for the test sequences used. The paper additionally highlights the need for further work in order to increase gains in perceived quality and optimise parameter selection.

When displaying Standard Dynamic Range (SDR) video on High Dynamic Range (HDR) displays a reverse tone mapping operation can be employed to expand the dynamic range of the SDR video to that offered by the display. This paper presents a subjective performance evaluation of existing reverse Tone Mapping Operators (rTMOs). The presented study evaluates the performance of rTMOs acting on both well exposed SDR video and video that exhibits exposure variation, as is often the case with user generated content (e.g. captured on mobile phones). The paper highlights flickering artefacts that arise in such cases and evaluates the effect on perceived quality of an adapted de-flickering method.

Encoding spatio-temporally varying textures is challenging for standardised video encoders, with significantly more bits required for textured blocks compared to non-textured blocks. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimize coding modes and performance. To this end, we examine the classification of video texture based on encoder performance. For this purpose, we employ spatio-temporal features and follow a two-step feature selection process by employing unsupervised machine learning approaches across the selected feature space. Finally, supervised machine learning approaches are applied on the set of the selected features that support classification prior to encoding with up to 95.1% accuracy. The results of this study offer the potential to underpin a new informed approach to a new informed approach to codec configuration and mode selection.

Previous work has shown that spatial resampling can improve rate-distortion performance by providing a higher and more consistent level of video quality at low bitrates. Rate control aims to regulate the video bitrate in accordance to the bit budget. While this is a well studied problem in the single resolution case, very little progress has been made on the adaptive resolution case. In this paper we present an enhanced method of rate control for intra coding that allows the algorithm to learn from previously coded frames and make more accurate predictions, resulting in a lower average mismatch ratio. Our main contribution, however, lies in our adaptive resolution approach where the best scale factor is selected after prediction of the best Quantisation Parameter (QP). We show that our method closely conforms to the bit budget and provides a more stable bitrate. The likelihood of frame skipping is therefore reduced and a more consistent level of video quality is provided compared to standard single resolution methods.

This paper investigates the effect that the QP offset value has on the coding performance of HEVC. We relate QP offset to the type of texture content present in the sequence. These then are used to develop a low-complexity adaptive QP offset selection method. This enables in-loop configuration of the QP offset parameter in a way that is content dependent and utilizes available encoding statistics. The proposed adaptive method is found to offer average bitrate reductions ranging from 1.38% for dynamic texture sequences up to 1.59% for static texture sequences relative to the QP offset used in the JCT-VC common test conditions.

In this paper, an extensive study of different video texture properties based on encoding statistics extracted from the HEVC HM reference software is presented. Mode selection, partitioning, motion vectors and bitrate allocation are among the statistics obtained from the encoder. For this study, a new dataset of homogeneous static and dynamic video textures, HomTex, is proposed. A comprehensive investigation of the results reveals a significant variability of coding statistics within dynamic textures, suggesting that this category should be further split into two relevant subcategories, continuous dynamic textures and discrete dynamic textures. This case is supported by an unsupervised learning approach on the statistics extracted. Finally, following the results obtained, some suggestions of improvements in video texture coding are presented.

Allocating low bit-budgets to intra-coded pictures can often lead to high quantisation and loss of important high frequency information. Coding at lower resolutions can alleviate some of this distortion when utilising effective resampling techniques and filters that minimise the additional distortion introduced as a result of spatial resampling. However, it is not always possible to reconstruct each portion of a picture to the same or similar level of quality; within natural scenes frequency content tends to vary spatially and therefore a fixed scale factor may either cause loss of high frequencies or oversampling of low frequency content. SHVC provides many of the tools required for enhancing rate-distortion performance of intra-coded pictures but it is not used for this purpose. In this paper we analyse the performance of a mixed resolution coding approach by using inter-layer prediction within SHVC to construct intra-coded pictures as a combination of different spatial resolutions. We evaluate the performance of coding with and without an enhancement layer for varying bit-budgets and compare these results to standard HEVC to provide insight regarding the optimisation of intra-coded pictures.

This work addresses the problem of predicting the compression efficiency of a video codec solely from features extracted from uncompressed content. Towards this goal, we have used a database of videos of homogeneous texture and extracted both spatial and frequency domain features. The videos are encoded using High Efficiency Video Coding (HEVC) reference codec at different quantization scales and their Rate-Distortion (RD) curves are modelled using linear regression. Using the extracted features and the fitted parameters of the RD model, a Support Vector Regression Model (SVRM) is trained to learn the relationship of the textural features with the RD curves. The SVRM is tested using iterative five-fold cross-validation. The presented experimental results demonstrate that RD curve characteristics can be predicted based on the textural features of the uncompressed videos, which offers potential benefits for encoder optimization.

This paper examines the performance of filter bank multicarrier (FBMC) systems for underwater acoustic communications. We evaluate the performance of Filtered Multitone (FMT) and Orthogonal Frequency Division Multiplexing-Offset Quadrature Amplitude Modulation (OFDM-OQAM) and compare it to that of traditional OFDM. OFDM-OQAM is found to be the most suitable multicarrier technique that can be applied to underwater acoustic communications as it achieves maximum bandwidth efficiency and provides a higher bit rate than its OFDM counterpart due to the absence of a cyclic prefix (CP). We also present a bit error rate (BER) performance evaluation of coded and un-coded OFDM-OQAM for both horizontally and vertically configured acoustic channels.

The human visual system (HVS) exhibits nonlinear sensitivity to the distortions introduced by lossy image and video coding. This effect is due to the luminance masking, contrast masking, and spatial and temporal frequency masking characteristics of the HVS. This paper proposes a novel perception-based quantization to remove nonvisible information in high dynamic range (HDR) color pixels by exploiting luminance masking so that the performance of the High Efficiency Video Coding (HEVC) standard is improved for HDR content. A profile scaling based on a tone-mapping curve computed for each HDR frame is introduced. The quantization step is then perceptually tuned on a transform unit basis. The proposed method has been integrated into the HEVC reference model for the HEVC range extensions (HM-RExt), and its performance was assessed by measuring the bitrate reduction against the HM-RExt. The results indicate that the proposed method achieves significant bitrate savings, up to 42.2%, with an average of 12.8%, compared with HEVC at the same quality (based on HDR-visible difference predictor-2 and subjective evaluations).

This paper presents a new publicly available video texture database (BVI Texture) that contains test sequences and subjective opinion scores. The database exhibits a wide range of static and dynamic textures together with some mixed content. Each sequence is indexed using various video feature descriptors that characterize its spatial activity, temporal activity, static texture content and dynamic texture content. Moreover, rate/distortion results for the new dataset are presented after compression using HEVC, alongside subjective quality evaluation data. The BVI texture database will provide utility in testing quality assessment metrics and emerging video compression methods, particularly those based on texture analysis and synthesis.

This paper presents a reduced reference video quality metric that exploits contrast and motion sensitivity characteristics of the HVS to perform a spatio-temporal selection of reference data. Spatio-temporal selection is realised through mapping of wavelet subbands to contrast sensitivity and through motion analysis. The proposed method is integrated with a modified SSIM-based framework to produce STIS-SSIM, a very low complexity reduced reference metric. The metric is shown to offer significant performance improvement over many existing full reference and reduced reference video quality metrics when tested on the LIVE video database.

This paper presents an end-to-end workflow for high dynamic range (HDR) content acquisition and HDR representation that aims to reproduce in a perceptually realistic manner the appearance of the captured scene. The proposed workflow includes a camera-independent colour calibration method for accurate HDR content acquisition and a perceptually optimized HDR signal representation method that takes into account display and viewing conditions. Subjective evaluation of the proposed and other methods indicate that the proposed workflow offers statistically significant improvements in the quality of the HDR image/video displayed to the viewers relative to the anchor methods.

Video transmission over error prone channels can suffer from packet losses when channel conditions are not favourable. As a result the quality of the decoded video at the receiver often differs from that of the encoded video at the transmitter. Accurate estimation of the end to end distortion (the distortion due to compression and packet loss after decoder error concealment) at the encoder can lead to more efficient and effective application of error resilience (e.g. selective intra coding, redundant slices etc.). This paper presents an end to end distortion estimation model that incorporates a probabilistic estimation of the distortion introduced by advanced error concealment methods often used by decoders, and new decaying factors, to mitigate the effect of packets loss. The proposed model offers significant improvements in estimation accuracy relative to existing models that only consider previous frame copy as the concealment strategy of the decoder.

Accurate prediction of the viewer's gaze location has the potential to improve bit allocation, rate control, error resilience and quality evaluation in video compression. With complex contexts, such as that of broadcast football video, the potential reward is even higher given that compression and transmission of this type of content is challenging. In this paper we propose a gaze location prediction system for high definition broadcast football video. The proposed system employs Bayesian integration of bottom-up features and context specific top-down cues. Our results show that the proposed model has better gaze prediction performance than other top-down models that we adapted to this context.

This paper proposes an efficient way of using edge based redundant information for improving the error resilience of IDR frames. The proposed method generates spatial error concealment mode and edge direction data at the encoder which are sent to the decoder to guide the concealment process. Results show that the method offers very good performance with only a very small increase in complexity at the decoder and the introduction of negligible overhead to the coded stream.

In this paper we present a study of objective video quality metric performance on test sequences which have undergone spatiotemporal resolution scaling prior to and after compression. The bit rates and resolutions tested have been chosen to typify consumer `home theatre' wireless streaming scenarios. The aim of the study is to identify a suitable video quality metric for use in a video streaming system that meets changing bandwidth constraints through adaptation of spatiotemporal resolution in addition to quantisation parameters. We first collect subjective quality scores and then assess correlation of objective metrics with this data. Of the metrics tested, we conclude that MOVIE is the most capable of accurate subjective score prediction under changing spatiotemporal resolution conditions.

High Dynamic Range (HDR) technology (capture and display), can offer high levels of immersion through a dynamic range that meets and exceeds that of the Human Visual System (HVS). This increase in immersion comes at the cost of higher bitrate requirements, which necessitate the development of efficient HDR-relevant coding solutions. Efficient perception-based compression of HDR imagery requires models that capture accurately the various masking effects experienced by the HVS under HDR conditions, so that bits are not wasted coding redundant imperceptible information. In this paper we present two psychovisual experiments that we carried out with the aid of a high dynamic range display, in order to determine potential differences between Standard Dynamic Range (SDR) and HDR edge masking (EM) and luminance masking (LM) effects. The EM experimental results indicate that the visibility threshold is higher for the case of HDR content than SDR, especially on the dark background side of an edge. The LM experimental results suggest that the HDR visibility threshold is higher compared to SDR for both dark and bright luminance backgrounds.

The Human Visual System (HVS) shows non-linear sensitivity to the distortion introduced by lossy image and video coding. This non-linear sensitivity is due to luminance masking, contrast masking and the spatial and temporal frequency masking phenomena of the HVS. This paper proposes a perception-based quantization method that exploits luminance masking in the HVS in order to enhance the performance of the High Efficiency Video Coding (HEVC) standard for the case of High Dynamic Range (HDR) video content. A profile scaling based on a tone-mapping curve computed for each HDR frame is introduced. The quantization step is then perceptually tuned on a Transform Unit (TU) basis. The proposed method has been integrated into the reference codec considered for the HEVC range extensions and its performance was assessed by measuring the bitrate reduction against the codec without perceptual quantization. The HDR-VDP-2 image quality metric was employed to measure the compressed picture quality. For the same quality level, an average bitrate reduction of 9% is achieved across all tested HDR sequences.

The sensitivity of the human visual system decreases dramatically with increasing distance from the fixation location in a video frame. Accurate prediction of a viewer's gaze location has the potential to improve bit allocation, rate control, error resilience, and quality evaluation in video compression. Commercially, delivery of football video content is of great interest because of the very high number of consumers. In this paper, we propose a gaze location prediction system for high definition broadcast football video. The proposed system uses knowledge about the context, extracted through analysis of a gaze tracking study that we performed, to build a suitable prior map. We further classify the complex context into different categories through shot classification thus allowing our model to prelearn the task pertinence of each object category and build the prior map automatically. We thus avoid the limitation of assigning the viewers a specific task, allowing our gaze prediction system to work under free-viewing conditions. Bayesian integration of bottom-up features and top-down priors is finally applied to predict the gaze locations. Results show that the prediction performance of the proposed model is better than that of other top-down models that we adapted to this context.

A channel adaptive multiple description video codec is presented with flexible redundancy allocation based on modeling and minimization of the end-to-end distortion. We employ a three-loop multiple description coding scheme for which we develop models that estimate the rate-distortion performance of the side encoders as well as the overall end-to-end distortion given channel statistics. A simple yet effective algorithm is formulated for determining appropriate levels of redundancy given a total bit rate and channel estimates in the form of packet error rates. The experimental results presented validate the proposed models over various channels conditions. The performance and adaptivity of the codec is evaluated through extensive simulations with a 22 wireless multiple input multiple output system. A gain of more than 10 dB can be achieved compared to a non-adaptive system and even larger gains can be had relative to typical single description transmissions.

This paper proposes a rate controlled redundancy-adaptive multiple description video coding method. The method adjusts the level of redundancy allocated to each macroblock based on an end to end distortion model that takes into account the effects of error propagation and concealment. Rate control at the macroblock level, ensures that the total bit-rate of all descriptions does not exceed the target rate. Results show that the proposed MDC codec can adapt very well to changing transmission conditions. When coupled with a MIMO architecture, performance gains of up to 14 dB were recorded over single description transmission at high packet error rates and no perceptible performance deficit at low packet error rates.

Systems with cheap/simple/power efficient encoders but complex decoders make applications such as low cost, low power remote sensors practical. Bandwidth considerations however are still an issue and compression efficiency has to remain high. In this paper, we present a distributed video codec (DVC) that we are developing with the aim of achieving such a low power paradigm at the cost of only a small compression performance deficit relative to the current state of the art, H.264. The proposed system employs spatial interleaving of KEY and Wyner-Ziv data which allows efficient side information (SI) generation through block-based error concealment, a Gray code that increases the accuracy of bit probability estimation, and a diversity scheme that produces more reliable results by exploiting multiple SI generated data. Simulation results show an improvement of the proposed scheme over H.264 intra coding of up to 1.5 dB. We additionally propose two mechanisms for selective parity bit feedback requests that can further reduce the WZ bitrate by up to 15%.

We propose a multicue gaze prediction framework for open signed video content, the benefits of which include coding gains without loss of perceived quality. We investigate which cues are relevant for gaze prediction and find that shot changes, facial orientation of the signer and face locations are the most useful. We then design a face orientation tracker based upon grid-based likelihood ratio trackers, using profile and frontal face detections. These cues are combined using a grid-based Bayesian state estimation algorithm to form a probability surface for each frame. We find that this gaze predictor outperforms a static gaze prediction and one based on face locations within the frame.

This paper presents a concealment based approach to distributed video coding that uses hybrid key/WZ frames via an FMO type interleaving of macroblocks. Our motivation stems from a previous work of ours that showed promising results relative to the more common approach of splitting the sequence in key and WZ frames. In this paper, we extend our previous scheme to the case of I-B-P frame structures and transform domain DVC. We additionally introduce a number of enhancements at the decoder including use of spatio-temporal concealment for generating the side information on a MB basis, mode selection for switching between the two concealment approaches and for deciding how the correlation noise is estimated, local (MB wise) correlation noise estimation and modified B frame quantisation. The results presented indicate considerable improvement (up to 30%) compared to corresponding frame extrapolation and frame interpolation schemes.

This paper proposes a gaze prediction model for open signed video content. A face detection algorithm is used to locate faces across each frame in both profile and frontal orientations. A grid-based likelihood ratio track before detect routine is used to predict the orientation of the signer's head, which allows the gaze location to be localised to either the signer or the inset. The face detections are then used to narrow down the gaze prediction further. The gaze predictor is able to predict the results of an eye tracking study with up to 95% accuracy, and an average accuracy of over 80%.

A novel technique for exploring the use of indexing metadata in improving coding efficiency is proposed in this paper. The technique uses an MPEG-7 descriptor as the basis for a fast mode decision algorithm for H.264/AVC encoders. The descriptor is used to form homogenous clusters for each frame, within which limited available coding modes for each macroblock are defined. The coding mode of an already coded macroblock that belongs to the same cluster in the same frame as well as the statistics of the coding modes of similar clusters in previous frames, are used for limiting the range of available coding modes within each cluster. The results show that the proposed algorithm is able to achieve an average of 47% time-saving when compared to the full search method and 21% when compared to the fast mode decision algorithm employed in the JM12.2 reference H.264 software encoder. In both cases, results yield only a small degradation in rate-distortion performance and a negligible loss in subjective quality.

The generation of the side information is an important part in the design of a distributed video coding (DVC) system as it directly relates to the system's rate distortion performance. In multi-view systems spatial/view inter-camera correlations can be exploited alongside temporal/motion intra camera ones for generating the side information as accurately as possible. In this paper, algorithms for fusing multiple such side information estimates, generated with temporal and view interpolation methods, are proposed. Two algorithms are suggested along with weighted fusion schemes for combining multiple methods. The results presented indicate performance improvements of up to 2 dB compared to exiting fusion approaches.

This paper proposes a concealment based approach to generating the side information and estimating the correlation noise for low-delay, pixel-based, distributed video coding. The proposed method employs a macroblock pattern similar to the one used in the dispersed type FMO of H.264 for grouping the macroblocks of each frame into intra coded (key) and Wyner-Ziv groups. Temporal concealment is then used at the decoder for "concealing" the missing macroblocks (estimating the side information-predicting the Wyner-Ziv macroblocks). The actual intra coded/decoded macroblocks are used for estimating the correlation noise. The results indicate significant performance improvements relative to existing motion extrapolation based approaches (up to 25% bit rate reduction).

MIMO (multiple-input-multiple-output) systems offer potential for throughput increase and enhanced quality of service for multimedia transmission. The underlying multipath environment requires new error-resilience techniques if the obtained benefits are to be fully exploited. Different MIMO architectures produce error-patterns of somewhat diverse characteristics. This paper proposes the use of multiple-description coding (MDC) as an approach that outperforms the standard-based error-resilience techniques in the majority of these cases. Results obtained from the random packet-error generator are furthered through the use of realistic MIMO channel scenarios and argue in favour of the deployment of an MDC-based video transmission system. Singular value decomposition (SVD) is used to create orthogonal sub-channels within a MIMO system which provide, depending on their respective gains and fading characteristics, an efficient means of mapping video content. Results indicate improvements in average PSNR of decoded test-sequences of up to 3 dB (5dB in the region of high PERs) compared to standard, single-description video transmission. This is also supported by significant subjective quality enhancements.

This article discusses a framework for model-based, context-dependent video coding based on exploitation of characteristics of the human visual system. The system utilizes variable-quality coding based on priority maps which are created using mostly context-dependent rules. The technique is demonstrated through two case studies of specific video context, namely open signed content and football sequences. Eye-tracking analysis is employed for identifying the characteristics of each context, which are subsequently exploited for coding purposes, either directly or through a gaze prediction model. The framework is shown to achieve a considerable improvement in coding efficiency.

This paper describes a modified rate control algorithm for H.264 that can accommodate multiple priority levels given a region of interest (Rol). The modified method allows better control of the quality of the RoI and gradual variation of the quality in the rest of the video frame through a bit redistribution process that is based on a number of parameters, including characteristics of the Rol, user input and perceptual factors.

This paper describes a spatial error concealment method that uses edge related information for concealing missing macroblocks in a way that not only preserves existing edges but also avoids introducing new strong ones. The method relies on a novel switching algorithm which uses the directional entropy of neighboring edges for choosing between two interpolation methods, a directional along detected edges or a bilinear using the nearest neighboring pixels. Results show that the performance of the proposed method is subjectively and objectively (PSNR wise) better compared to both 'single interpolation' and to edge strength based switching methods

Delay sensitive video transmission over error prone networks can suffer from packet erasures when channel conditions are not favorable. Use of error concealment (EC) at the video decoder is necessary in such cases to prevent error induced artefacts making the affected video frames visibly intolerable. This paper proposes an EC method that incorporates enhanced temporal and spatial concealment elements, the use of which is controlled by a mode selection (MS) algorithm well matched to the characteristics of the temporal concealment approach. The performance of the individual enhancements and of the MS algorithm are compared with the respective features of the method employed in the H.264 joint model (JM) decoder and with other state of the art methods. The overall performance of the proposed method is shown to offer significant gains (up to 9 dB) compared to that of the JM decoder for a wide range of natural and animation image sequences without any considerable increase in complexity

This paper develops error concealment methods for multiple description video coding (MDC) in order to adapt to error prone packet networks. The three-loop slice group MDC approach of D. Wang et al. (2005) is used. MDC is very suitable for multiple channel environments, and especially able to maintain acceptable quality when some of these channels fail completely, i.e. in an on-off MDC environment, without experiencing any drifting problem. Our MDC scheme coupled with the proposed concealment approaches proved to be suitable not only for the on-off MDC environment case (data from one channel fully lost), but also for the case where only some packets are lost from one or both channels. Copying video and using motion vectors from correct descriptions are combined together for concealment prior to applying traditional methods. Results are compared to the traditional error concealment method proposed in the H.264 reference software, showing significant improvements for both the balanced and unbalanced channel cases.

The ability to communicate remotely through the use of video as promised by wireless networks and already practised over fixed networks, is for deaf people as important as voice telephony is for hearing people. Sign languages are visual-spatial languages and as such demand good image quality for interaction and understanding. In this paper, based on analysis of the viewer's perceptual behavior and the video content involved we propose a sign language video coding system using foveated processing, which can lead to bit rate savings without compromising the comprehension of the coded sequence. We support this claim with the results of an initial comprehension assessment trial of such coded sequences by deaf users.

Three-dimensional wavelet coding of volumetric medical images provides better coding performance compared to corresponding 2D methods by exploiting the inter-slice correlation that exists in such data. It introduces however latencies when it comes to transmitting specific parts of the volume. This paper presents an extension to 3D-SPlHT which allows 3D region of interest (ROI) coding. ROl coding enables faster reconstruction of diagnostically useful regions in volumetric datasets by assigning higher priority to them in the bitstream. It also introduces the possibility for increased compression performance, by allowing certain parts of the volume to be coded in a lossy manner while others are coded losslessly. The necessary modifications to 3D-SPIHT for ROI coding are described and methods for specifying a 3D ROI without adding a significant overhead are suggested. Results are presented highlighting the benefits of the ROl extension.

This paper presents results in the image processing and visualization aspects of a virtual liver biopsy system (a system for simulating the medical procedure of liver biopsy). The creation of 3D models from 2D images of the organs involved is described, and segmentation requirements of this process are discussed. Endoscopic images of the liver that simulate the needle's point of view are created by means of combined volume and surface rendering. For this purpose ray casting is used with the ray start and end points being constrained within a surface rendered environment. Visualization of the needle insertion process from an exterior point of view is presented. A real-time sectional imaging component is also used in which the displayed 2D-image section of the 3D volume tracks the tip of the needle.