Intra prediction is an important tool in intra-frame video coding to reduce the spatial redundancy. In current coding standard H.265/high-efficiency video coding (HEVC), a copying-based method based on the boundary (or interpolated boundary) reference pixels is used to predict each pixel in the coding block to remove the spatial redundancy. We find that the conventional copying-based method can be further improved in two cases: 1) the boundary has an inhomogeneous region and 2) the predicted pixel is far away from the boundary that the correlation between the predicted pixel and the reference pixels is relatively weak. This paper performs a theoretical analysis of the optimal weights based on a first-order Gaussian Markov model and the effects when the pixel values deviate from the model and the predicted pixel is far away from the reference pixels. It also proposes a novel intra prediction scheme based on the analysis that smoothing the copying-based prediction can derive a better prediction block. Both the theoretical analysis and the experimental results show the effectiveness of the proposed intra prediction method. An average gain of 2.3% on all intra coding can be achieved with the HEVC reference software.

360 degrees video is emerging as a new way of experiencing immersive video due to the ready availability of powerful handheld devices such as smartphones. 360 degrees video enables immersive “real life”, “being there” experience for consumers by capturing the 360 degree view of the world. Users can change their viewpoint and dynamically view any part of the captured scene they desire. 360 degrees video requires higher bitrate than conventional video due to increased video resolution (4K and beyond) needed to support the wider field of view. Efficient compression of 360 degrees video is thus needed to provide high quality immersive video viewing experience to consumers. This paper provides an overview of 360 degrees video and presents a region adaptive video smoothing technique that exploits the unique characteristics of 360 degrees video to provide up to 20% bitrate savings with minimal perceptual quality degradation while requiring no modifications to the video decoder.

Video captured through fisheye lens is becoming very prevalent due to applications such as surveillance, automotive driver assistance systems, and recreational sports. Virtual reality applications such as 360 degrees video also use fish-eye lens video cameras to capture the 360 degree view. The main advantage of fisheye lenses is that they increases the field of view, however they also introduce warping distortion in the captured video. Due to the warping, the motion in the video is typically non-translational and traditional block motion compensation techniques are not fully effective for such video. This paper presents a warping motion compensation technique that models the fish-eye lens distortion to efficiently code warped video. Given the fisheye lens parameters (e.g., transmitted at a video sequence level), the warping model is implicitly derived in both the encoder and decoder and no block-level warping parameters are transmitted. The proposed approach was integrated into HEVC HM-14.0 and initial results on simulated fish-eye lens distorted video show promise especially when the global motion in the video is fast.

In this paper, we present a low-complexity loop filter for video coding. We begin by presenting a set of non-Wiener based loop filters that can complement the Wiener-based adaptive loop filter which was considered as a tool for possible adoption in the HEVC standard. We devise filters which can be adaptively operated on different regions in an image. We devise a quad-tree based signaling for the filters, and present various loop filters: such as bilateral and Gaussian, as well as a separable 3-tap filter which can be implemented by just shifts and adds. The proposed 3-tap filter is thus hardware friendly with minimal complexity. In terms of compression performance, the proposed 3-tap filter can approach other sophisticated filters, albeit at a substantially reduced complexity; and can provide compression gains of 2.3% on average; and upto 7.0% for Low-Delay-P configuration over a data-set of 19 diverse HD, and UHD sequences of upto 8K resolution when implemented on top of the HM14.0 software for HEVC. Finally, we also present results for combining our proposed filters with the Wiener-based adaptive loop filter considered in HEVC, and illustrate that there is a significant amount of compression gain that can be achieved by loop filters for the next generation of video coding standard beyond HEVC.

The Intra Block Copy (IntraBC) is a newly adopted tool in the HEVC extension for the screen content video coding. The IntraBC tool efficiently encodes repeating patterns in a picture. The current IntraBC scheme achieves about 1.0% bit-rate reduction on average and up to 4.3% bitrate reduction on natural content video for a database consisting of 2K, 4K, and 8K sequences. In this paper, we propose to improve the IntraBC with a template matching block vector and a fractional search IntraBC. With these two tools, the gain on natural content video coding can be further improved by 0.5% on average and up to 2.0%.

There has been considerable interest in distributed source coding (DSC) in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat to practical deployment of such techniques: the exponential growth of decoding complexity with network size and coding rates and the critical requirement of resilience to bit errors and erasures, given the severe channel conditions in many wireless sensor network applications. This article proposes a novel, unified approach for large-scale, error/erasure-resilient DSC that incorporates an optimally designed, nearest neighbor classifier-based decoding framework, where the design explicitly controls performance versus decoding complexity. Motivated by the highly nonconvex nature of the cost function, we present a deterministic annealing-based optimization algorithm for the joint design of the system parameters, which further enhances the performance over the greedy iterative descent technique. Simulation results on both synthetic and real sensor network data provide strong evidence for performance gains compared to other state-of-the-art techniques and may open the door to practical deployment of DSC in large sensor networks. Moreover, the framework provides a principled way to naturally scale to large networks while constraining decoder complexity, thereby enabling performance gains that increase with network size.

Screen content video coding is becoming increasingly important in various applications, such as desktop sharing, video conferencing, and remote education. In general, compared to natural camera-captured content, screen content has different characteristics, such as sharp edges. In this paper, we propose a novel intra prediction scheme for screen content video. In the proposed scheme, bilinear interpolation in angular intra prediction in HEVC is selectively replaced by nearest-neighbor (NN) interpolation to preserve the sharp edges in screen content video. We present two different variants of NN interpolation. In the first implicit pixel-based method, both the encoder, and the decoder determine whether to perform NN interpolation based on the prediction pixels. The second method comprises of the encoder performing a Rate-Distortion search at a block-level, and explicitly signaling a flag to the decoder to indicate when to use the NN interpolation. Both the proposed variants provide significant gains over HEVC, and simulation results show that average gains of 3.3% BD-bitrate are achieved for screen content video. The HEVC proposal of this method was accepted in the core experiments, and would be a technology under consideration in the ongoing Screen Content Coding extension of HEVC scheduled to begin in March 2014.

Screen content video coding is becoming increasingly important in various applications, such as desktop sharing, video conferencing, and remote education. In January 2014, the ITU-T and ISO/IEC MPEG jointly issued call for proposals for screen content coding as an extension of HEVC. In general, compared to natural camera-captured video content, screen content has different characteristics, e.g., sharper edges, and fewer unique colors on a block-by-block basis. Palette coding for screen content has been recently proposed in the ongoing HEVC Range Extensions standardization. Palette coding utilizes the fact that there are few unique colors in screen content video blocks, and tries to send palettes of these unique colors. However, the size of these palettes can expand, especially in highresolution video. Therefore, to reduce the overall bits for palette transmission, we present various palette prediction techniques in this paper. We first analyze the characteristics of palettes in screen content, and then propose various palette prediction techniques. Our experiments on HEVC test sequences in state-of-the-art HM range extensions software for HEVC show significant improvements over palette coding without any prediction, at almost negligible additional complexity.

In this paper, we provide an overview of the DCT/DST transform scheme for intra coding in the HEVC standard. A unique feature of this scheme is the use of DST-VII transforms in addition to DCT-II. We further derive factorizations for fast joint computation of DCT-II and DST-VII transforms of several sizes. Simulation results for the DCT/DST scheme in the HM reference software for HEVC are also provided together with a discussion on computational complexity.

As part of a larger research agenda to explore web development as a context for learning computational literacy skills, we investigate errors people make while writing code in HTML and CSS. We report on a lab-based study in which 20 participants were video recorded as they completed coding tasks. We have applied the skills-rules-knowledge framework to segment this data by the cognitive causes of errors they made, and present a taxonomy of these errors. Our findings demonstrate how the skills-rules-framework can be used to analyze coding errors, provide insight about the origins of these errors, and suggest ways that the design of web development tools can be improved to support learning and practice with HTML and CSS.

In this paper, we present a secondary transform scheme for inter-layer prediction residue in scalable video coding (SVC). Efficient prediction of the co-located blocks from the base layer (BL) can significantly improve the enhancement layer (EL) coding in SVC, especially when the temporal information from previous EL frames is less correlated than the co-located BL information. Previously, Guo et al. showed that because of the peculiar frequency characteristics of EL residuals, the conventional DCT Type-2 transform is suboptimal and is often outperformed by either the DCT Type-3, or DST Type-3 when these transforms are applied to the EL residuals. However, their proposed technique requires upto 8 additional transform cores, two of which are of size 32×32. Here, in this work, we propose a low-complexity secondary transform scheme, where the proposed transform is applied only to the lower frequency coefficients after DCT. Our proposed transform scheme requires at most only 2 additional cores. We also present a fast implementation for a class of 4×4 secondary transforms, which actually is a cascade of two DCT Type-2 using only 6 multiplications. Simulation results show that our proposed scheme provides significant BD-Rate improvement over the conventional DCT-based coding scheme in the ongoing scalable extensions of HEVC standardization.

The objective in developing compact descriptors for visual image search is building an image retrieval system that works efficiently and effectively under bandwidth and memory constraints. Selecting local descriptors to be processed, and sending them to the server for matching is an integral part of such a system. One such image search and retrieval system is the Compact Descriptors for Visual Search (CDVS) standardization test model being developed by MPEG which has an efficient local descriptor selection criteria. However, all the existing selection parameters in CDVS are based on low-level features. In this paper, we propose two “mid-level” local descriptor selection criteria: Visual Meaning Score (VMS), and Visual Vocabulary Score (VVS) which can be seamlessly integrated into the existing CDVS framework. A mid-level criteria explicitly allows selection of local descriptors closer to a given set of images. Both VMS and VVS are based on visual words (patches) of images, and provide significant gains over the current CDVS standard in terms of matching accuracy, and have very low implementation cost.

Image matching and search is gaining significant commercial importance nowadays due to various applications it enables such as augmented reality, image-queries for internet search, etc. Many researchers have effectively used color information in an image to improve its matching accuracy. These techniques, however, cannot be directly used for large scale mobile visual search applications that pose strict constraints on the size of the extracted features, computational resources and the system accuracy. To overcome this limitation, we propose a new and effective technique to incorporate color information that can use the SIFT extraction technique. We conduct our experiments on a large dataset containing around 33, 000 images that is currently being investigated in the MPEG-Compact Descriptors for Visual Search Standard and show substantial improvement compared to baseline.

In this paper, we present a secondary transform scheme for inter-layer prediction residue in scalable video coding (SVC). Efficient prediction of the co-located blocks from the base layer (BL) can significantly improve the enhancement layer (EL) coding in SVC, especially when the temporal information from previous EL frames is less correlated than the co-located BL information. However, Guo et al. showed that because of the peculiar frequency characteristics of EL residuals, the conventional DCT Type-2 transform is suboptimal and is often outperformed by either the DCT Type-3, or DST Type-3 when these transforms are applied to the EL residuals. However, their proposed technique requires upto 8 additional transform cores, two of which are of size 32×32. Here, in this work, we propose a secondary transform scheme, where the proposed transform is applied only to the lower 8x8 frequency coefficients after DCT, for block sizes 8×8 to 32×32. Our proposed transform scheme requires at most only 2 additional cores. We also propose a low-complexity 8x8 Rotational Transform as a special case of secondary transforms in this paper. Simulation results show that the proposed transform scheme provides significant BD-Rate improvement over the conventional DCT-based coding scheme for video sequences in the ongoing scalable extensions of HEVC standardization.

In this paper, we present a DCT/DST based transform scheme that applies either the conventional DCT or type-7 DST for all the video-coding intra-prediction modes: vertical, horizontal, and oblique. Our approach is applicable to any block-based intra prediction scheme in a codec that employs transforms along the horizontal and vertical direction separably. Previously, Han, Saxena, and Rose showed that for the intra-predicted residuals of horizontal and vertical modes, the DST is the optimal transform with performance close to the KLT. Here, we prove that this is indeed the case for the other oblique modes. The optimal choice of using DCT or DST is based on intra-prediction modes and requires no additional signaling information or rate-distortion search. The DCT/DST scheme presented in this paper was adopted in the HEVC standardization in March 2011. Further simplifications, especially to reduce implementation complexity, which remove the mode-dependency between DCT and DST, and simply always use DST for the 4 × 4 intra luma blocks, were adopted in the HEVC standard in July 2012. Simulation results conducted for the DCT/DST algorithm are shown in the reference software for the ongoing HEVC standardization. Our results show that the DCT/DST scheme provides significant BD-rate improvement over the conventional DCT based scheme for intra prediction in video sequences.

In this paper, we present a transform scheme where a secondary transform is applied after the conventional DCT for intra as well as inter prediction residues. Our approach is applicable to any block-based video codec that employs transforms along the horizontal and vertical direction separably. The secondary transform is applied to the lower K ( K=4 or 8) frequency coefficients of the output of conventional DCT at block with dimensions 8 and larger. The proposed transform scheme has low complexity as it is applied only to the top-left portion of the DCT output, especially in the context of large blocks such as 32 × 32 where an alternate non-DCT 32 × 32 transform would have a prohibitive implementation hardware cost. The proposed technique is single-pass, and the choice of whether to use the secondary transform is solely based on the prediction direction for intra residue, and on transform unit location in the prediction unit for the inter residue. The scheme requires no additional signaling information or R-D search. Our simulation results show that the proposed transform scheme provides significant BD-rate improvement over the conventional DCT-based coding scheme. Finally, we also show how to implement the proposed secondary transforms with low latency in hardware.

In this paper, we present a secondary transform that is applied in a codec after the conventional DCT for all the video-coding intra prediction modes. Our approach is applicable to any block-based intra prediction scheme that employs transforms along the horizontal and vertical direction separably. The secondary transform is applied to the lower 8×8 frequency coefficients of the output of conventional DCT at block sizes 8×8 and higher. The proposed transform scheme has low complexity as it is applied only to the top-left portion of DCT output, especially in the context of large blocks such as 32×32 where an alternate transform of size 32×32 other than DCT would be expensive to be implemented in hardware. The proposed technique works in single-pass, and the choice of when to use the secondary transform is solely based on the intra prediction mode and requires no additional signaling information or R-D search. Our simulation results show that the proposed transform scheme provides significant BD-Rate improvement over the conventional DCT-based coding scheme for intra prediction of video sequences in the ongoing HEVC standardization.

In this paper, we present a novel video fingerprinting algorithm which leverages the concept of perceptual similarity between different video sequences. Inspired by the popular structural similarity (SSIM) index, we quantify the perceptual similarity between different video sequences by proposing a perceptual distance metric (PDM) which is utilized in the matching stage of our proposed video fingerprinting algorithm. PDM requires very simple features, viz., block means and therefore has extremely low complexity in both the feature extraction part, as well as during the matching stage. We also show how to use an order statistic in the proposed distance measure to improve the system performance for localized block-based artifacts such as the logo artifact. Simulation results for the proposed fingerprinting algorithm show significant gains over other video fingerprinting techniques on different video datasets for numerous heavy video artifacts.

In this paper, we present a transform scheme that utilizes a secondary transform after the conventional DCT for intra as well as inter prediction residues. Our approach is applicable to any block-based video codec that employs transforms along the horizontal and vertical direction separably. The secondary transform is applied to the lower K (K=4 or 8) frequency coefficients of the output of conventional DCT at block with dimensions 8 and higher. The proposed transform scheme has low complexity as it is applied only to the top-left portion of DCT output, especially in the context of large blocks such as 32×32 where an alternate transform of size 32×32 other than DCT would be expensive to be implemented in hardware. The proposed technique works in single-pass, and the choice of whether to use the secondary transform is solely based on the prediction direction for intra residue, and transform unit location in the prediction unit for the inter residue. The scheme requires no additional signaling information or R-D search. Our simulation results show that the proposed transform scheme provides significant BD-Rate improvement over the conventional DCT-based coding scheme for video sequences in the ongoing HEVC standardization.

There has been considerable interest in distributed source coding within the compression and sensor network research communities in recent years, primarily due to its potential contributions to low-power sensor networks. However, two major obstacles pose an existential threat on practical deployment of such techniques in real world sensor networks, namely, the exponential growth of decoding complexity with network size and coding rates, and the critical requirement for error-resilience given the severe channel conditions in many wireless sensor networks. Motivated by these challenges, this paper proposes a novel, unified approach for large scale, error-resilient distributed source coding, based on an optimally designed classifier-based decoding framework, where the design explicitly controls the decoding complexity. We also present a deterministic annealing (DA) based global optimization algorithm for the design due to the highly non-convex nature of the cost function, which further enhances the performance over basic greedy iterative descent technique. Simulation results on data, both synthetic and from real sensor networks, provide strong evidence that the approach opens the door to practical deployment of distributed coding in large sensor networks. It not only yields substantial gains in terms of overall distortion, compared to other state-of-the-art techniques, but also demonstrates how its decoder naturally scales to large networks while constraining the complexity, thereby enabling performance gains that increase with network size.

This paper proposes a novel approach to jointly optimize spatial prediction and the choice of the subsequent transform in video and image compression. Under the assumption of a separable first-order Gauss-Markov model for the image signal, it is shown that the optimal Karhunen-Loeve Transform, given available partial boundary information, is well approximated by a close relative of the discrete sine transform (DST), with basis vectors that tend to vanish at the known boundary and maximize energy at the unknown boundary. The overall intraframe coding scheme thus switches between this variant of the DST named asymmetric DST (ADST), and traditional discrete cosine transform (DCT), depending on prediction direction and boundary information. The ADST is first compared with DCT in terms of coding gain under ideal model conditions and is demonstrated to provide significantly improved compression efficiency. The proposed adaptive prediction and transform scheme is then implemented within the H.264/AVC intra-mode framework and is experimentally shown to significantly outperform the standard intra coding mode. As an added benefit, it achieves substantial reduction in blocking artifacts due to the fact that the transform now adapts to the statistics of block edges. An integer version of this ADST is also proposed.

Canonical distributed quantization schemes do not scale to large sensor networks due to the exponential decoder storage complexity that they entail. Prior efforts to tackle this issue have largely been limited to the suboptimal schemes of source grouping and decoding, thus failing to use all available information at the decoder. We propose a new decoding paradigm where all received bits are used in decoding. Essentially, to decode each source, we partition the space of received bit-tuples using a nearest neighbor quantizer at a decoding rate consistent with the allowed complexity and each partition is then mapped to a reconstruction value for that source. To avoid local minima in design, we resort to deterministic annealing to determine the nearest neighbor partition function (the partitioning prototypes) from the training set. Results on several data-sets show substantial gains over naive and other competing approaches.

In this paper, we present a mode-dependent transform scheme that applies either the conventional DCT or type-7 DST for all the video-coding intra-prediction modes: vertical, horizontal or oblique. Our approach is applicable to any block-based intra prediction scheme in a codec, that employs transforms along the horizontal and vertical direction separably. Previously Han, Saxena & Rose showed that for the intra-predicted residuals of horizontal and vertical modes, the DST is the optimal transform with performance close to KLT. Here we prove that this is indeed the case for the other oblique modes. The choice of using DCT/DST is based on intra-prediction modes, and requires no additional signaling information or Rate-Distortion search. Simulations are conducted for the DCT/DST algorithm in TMuC 0.9, the reference software for the ongoing HEVC standardization. Our results show that the DCT/DST scheme provides significant BD-Rate improvement over the DCT for intra prediction in video sequences.

This paper proposes a new approach to combined spatial (Intra) prediction and adaptive transform coding in block-based video and image compression. Context-adaptive spatial prediction from available, previously decoded boundaries of the block, is followed by optimal transform coding of the prediction residual. The derivation of both the prediction and the adaptive transform for the prediction error, assumes a separable first-order Gauss-Markov model for the image signal. The resulting optimal transform is shown to be a close relative of the sine transform with phase and frequencies such that basis vectors tend to vanish at known boundaries and maximize energy at unknown boundaries. The overall scheme switches between the above sine-like transform and discrete cosine transform (per direction, horizontal or vertical) depending on the prediction and boundary information. It is implemented within the H.264/AVC intra mode, is shown in experiments to significantly outperform the standard intra mode, and achieve significant reduction of the blocking effect.

This paper considers the problem of distributed source coding for a large sensor network. A typical shortcoming of current approaches to true distributed coding is the exponential growth of the decoder codebook size with the number of sources in the network. This growth in complexity renders many traditional approaches impractical for even moderately sized sensor networks. Inspired by our recent results on fusion coding for selective retrieval, we propose a new distributed coding approach that scales to a large number of sources. Central to our approach is a “bit-subset selector” module whose role is to judiciously extract an appropriate subset of the received bits for decoding per individual source. This, together with joint design of all system components, enables direct optimization of the decoder complexity-distortion tradeoff, and thereby the desired scalability. Experiments on both real and synthetic data-sets show considerable gains over heuristic schemes.

Motivated by the paradigm of event-based monitoring, which can potentially alleviate the inherent bandwidth and energy constraints associated with wireless sensor networks, we consider the problem of joint coding of correlated sources under a cost criterion that is appropriately conditioned on event occurrences. The underlying premise is that individual sensors only have access to partial information and, in general, cannot reliably detect events. Hence, sensors optimally compress and transmit the data to a fusion center, so as to minimize the expected distortion in segments containing events. In this work, we derive and demonstrate the approach in the setting of entropy constrained distributed vector quantizer design,using a modified distortion criterion that appropriately accounts for the joint statistics of the events and the observation data. Simulation results show significant gains over conventional design as well as existing heuristic based methods, and provide experimental evidence to support the promise of our approach.

This paper considers the problem of scalable distributed coding of correlated sources that are communicated to a central unit. The general setting is typically encountered in sensor networks. The conditions of communication channels between the source encoders and fusion center may be time-varying and it is often desirable to guarantee a base layer of coarse information during channel fades. In addition the desired system should be robust to various scenarios of channel failure and should utilize all the available information to attain the best possible compression efficiency. Although a standard dasiaLloyd-stylepsila distributed coder design algorithm can be generalized to scalable distributed source coding, the resulting algorithm depends heavily on initialization and will virtually always converge to a poor local minimum on the distortion-cost surface. We propose an efficient initialization scheme for such a system, which employs a properly designed multi-stage distributed coder. In our prior work, we highlighted the fundamental conflict that arises when multi-stage coding is directly combined with distributed quantization. Here we use the multi-stage distributed coding system to initialize a scalable distributed coder and propose an iterative algorithm for joint design of all system components once the structural constraint is removed. Simulation results show considerable gains over randomly initialized scalable distributed coder design.

This paper considers the problem of distributed scalable coding of correlated sources that are communicated to a central unit. The general setting is typically encountered in sensor networks. The conditions of communication channels between the sensor sources and fusion center may be time-varying and it is often desirable to guarantee a base layer of coarse information during channel fades. Specifically, we consider a multi-stage coding system to perform such distributed scalable coding of correlated sources. This problem poses new challenges. We show that mere extensions of distributed coding ideas to include multi-stage coding yield poor rate-distortion performance, due to underlying conflicts between the objectives of scalable and distributed coding. An appropriate system paradigm is developed which allows such tradeoffs to be explicitly controlled within joint optimization of all the system components. We propose an iterative joint design technique and derive the necessary conditions for optimality which yield its update rules. Simulation results show substantial gains over single source (separate) multi-stage coding as well as naive extensions to incorporate scalability in distributed scalable coding schemes.

Modern video compression includes a mature set of codecs, tools, and techniques that correspond to a variety of coding efficiencies and royalty costs in video delivery. In early work we showed that there can be significant benefits to employing a royalty-aware encoder that jointly optimizes rate, distortion, and royalty cost by choosing among several codecs having different royalty costs [1]. In this paper we assume the existence of such an encoder and concentrate on how royalty costs should be assigned to benefit a system of users, video service providers, and tool/codec owners. Our work operates on the intersection of rate-distortion principles with basic concepts from economics and tries to address issues that are rapidly becoming very relevant in media delivery. Using a simple model that captures user preferences of video quality levels (given costs associated with these levels), we formulate the assignment of royalty costs as an optimization problem and solve it for optimal costs for several scenarios. The main trade-offs that govern our results are the cost of bandwidth and previously unachievable quality levels (if any) that a state-of-the-art codec gives access to. We show that content-based cost assignment not only increases user utilization but also the royalty earnings for tool owners compared to content-unaware pricing policies.

This paper investigatesdistributedpredictivecoding of correlated sources with memory, which are communicated to a central receiver. This is the setting typically encountered in sensor networks. While source memory may be exploited by distributed coding of large source blocks (vectors), the growth in complexity (and delay) is often unacceptable in practice, hence the interest in a low complexity predictive approach. We first consider the inherent "conflict" between distributed and predictive coding due to the impact of distributed quantization on the prediction loop. This is coupled with the effects of closed loop prediction, which destabilize standard Lloyd-like code design methods. An iterative algorithm is derived, which optimizes the overall system while imposing zero decoder drift due to distributed quantization. The approach circumvents convergence and stability issues of traditional predictive quantizer design by employing an "asymptotic closed loop" framework which is adapted for distributed predictive system design. The scheme efficiently utilizes both the temporal and inter-source correlations and subsumes as extreme special cases both separate source predictive coding, and distributed coding of memoryless correlated sources.

This paper considers the design of efficient quantizers for a distributed source coding system. The information is encoded at independent terminals and transmitted across separate channels, any of which may fail. The scenario subsumes a wide range of vector quantization problems. Greedy descent methods rely heavily on initialization, and the presence of numerous 'poor' local optima on the distortion cost surface strongly motivates the use of a global design algorithm. We propose a deterministic annealing approach for the design of all components of a generic distributed source coding system. Our approach avoids many poor local optima, is independent of initialization, and does not assume any prior information on the underlying source distribution. Simulation results show significant gains over an iterative greedy algorithm.

The focus of this work is on the design of efficient quantizers for correlated sources subject to complexity limitations on the encoding terminals. Existing iterative descent methods rely heavily on initialization, and the prevalence of numerous 'poor' local optima strongly motivates the use of a global design algorithm. We propose a multiple-prototype based deterministic annealing approach for the joint design of all components of a generic distributed source coding system. Our approach avoids many poor local optima, is independent of initialization and does not assume any prior information on the underlying source distribution. Simulation results show substantial gains over a Lloyd-like iterative algorithm