This paper describes Slepian-Wolf codes based on overlapped quasi-arithmetic codes, where overlapping allows lossy compression of the source below its entropy. In the context of separate decoding, these codes are not uniquely decodable: the overlap introduces ambiguity in the decoding process leading to decoding errors. The presence of correlated side information at the decoder is used to remove this ambiguity and achieve a vanishing error probability. The state models and the automata of the overlapped quasi-arithmetic codes are described. The soft decoding algorithm with side information is then presented. The performance of these codes has been assessed first on theoretical sources and integrated in a distributed video coding platform.
Distributed Video Coding (DVC) is a codingpmadigm that gives the decoder the task to exploit the source slatistics to achieve eficient compression. Current approaches to D VC rely on mo tion-compensated interpolation to generate at the decoder an estimation of the f?me being decoded. This paper presents an iterative motion-compensated interpolation technique that takes advantage of all available information about the pame being estimated, not only the previous and posterior j a m e s as is common practice. Simulation results show that the addition of this estimation technique to an existing DYC codecproduces a 0.15 dB improvement in the PSNR of the rate-distortion plots. Furthermore, U method to avoid ming the return channel existing in some D VC implementations is presented that only incurs in a penalty of I O to IO0 bit/!.
Abstract-This paper considers the use of punctured quasiarithmetic (QA) codes for the Slepian-Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original message in order to remove this ambiguity. A complete distributed source coding (DSC) scheme based on QA encoding with side information at the decoder is presented, together with iterative structures based on QA codes. The proposed schemes are adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed schemes are efficient in terms of decoding performance for short sequences compared to well-known DSC solutions using channel codes.
Abstract-This paper considers the use of punctured quasiarithmetic (QA) codes for the Slepian-Wolf problem. These entropy codes are defined by finite state machines for memoryless and first-order memory sources. Puncturing an entropy coded bit-stream leads to an ambiguity at the decoder side. The decoder makes use of a correlated version of the original message in order to remove this ambiguity. A complete distributed source coding (DSC) scheme based on QA encoding with side information at the decoder is presented, together with iterative structures based on QA codes. The proposed schemes are adapted to memoryless and first-order memory sources. Simulation results reveal that the proposed schemes are efficient in terms of decoding performance for short sequences compared to well-known DSC solutions using channel codes.
Distributed Source Coding (DSC), formulated thirty years ago, is lately witnessing a great research effort. This effort is encouraged by emerging new applications that could greatly benefit from the properties inherent to DSC, like low complexity encoders and embedded error resilience.Among the challenging topics related to DSC there is the generation of the Side Information, an estimation made by the decoder of the information being decoded.This paper quickly summarizes the theoretical bases of DSC and then focuses on the essential task of generating the Side Information, by developing the model-based method drafted by the authors on a previous publication. It is shown that the application of model-based Side Information generation techniques on top of current state of the art DSCbased video codecs provides an improvement of 0.5 to 0.75dB over a wide range of bit-rates. Comparisons against H.264 are also provided.
Distributed Video Coding (DVC) is a coding paradigm that gives the decoder the task to exploit the source statistics to achieve efficient compression. Many approaches to the DVC problem have recently appeared in the literature, including the PRISM codec. Instead of encoding the deterministic quantized prediction error residual, PRISM partitions the quantization lattice into cosets and sends the index of the coset each quantized coefficient belongs to. Estimating the number of cosets is of crucial importance to achieve good coding efficiency. In PRISM, this is determined during an offline training phase. The present work aims at being a starting point for the suppression of the training stage of PRISM at the cost of sending the number of cosets for each DCT coefficient. The statistics of the number of cosets are analyzed to figure out the maximum compression efficiency achievable by entropy coding. Furthermore the paper discusses some techniques that might be used to lower the amount of transmitted bits. Based on these results, directions for future works are proposed.
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