We propose a new architecture for distributed image compression from a group of distributed data sources. The proposed architecture, which we refer to as symmetric Encoder-Decoder Convolutional Recurrent Neural Network, is able to significantly outperform the state-of-the-art compression techniques such as JPEG on rate-distortion curves. We also show that by training distributed encoders and joint decoders on correlated data sources, the performance of compression is much better than that by training codecs separately. For 10 distributed sources, our distributed system remarkably performs within 2 dB peak signal-to-noise ratio (PSNR) of that of a single codec trained with all data sources. We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC). Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources. To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with Deep Learning.
Though there has been a variety of research on lossy data compression in the past few decades, little attention has been paid to a systematic approach for general and practical distributed code design, especially in the presence of an arbitrary number of nontrivial data sources with arbitrary correlation @cite_24 . A main motivation of this work is to attempt to replace the practical hand-crafted code design with data-driven approaches. To our best knowledge, what we propose is the first data-driven DSC architecture. Unlike hand-crafted quantizers, our neural network-based quantizers show that the correlations among different data sources can be trained by the model parameters. We empirically show that the Slepian-Wolf limit can be achieved with our methodology.
@cite_24
2162404506
In recent years, sensor research has been undergoing a quiet revolution, promising to have a significant impact throughout society that could quite possibly dwarf previous milestones in the information revolution. Realizing the great promise of sensor networks requires more than a mere advance in individual technologies. It relies on many components working together in an efficient, unattended, comprehensible, and trustworthy manner. One of the enabling technologies in sensor networks is the distributed source coding (DSC), which refers to the compression of the multiple correlated sensor outputs that does not communicate with each other. DSC allows a many-to-one video coding paradigm that effectively swaps encoder-decoder complexity with respect to conventional video coding, thereby representing a fundamental concept shift in video processing. This article has presented an intensive discussion on two DSC techniques, namely Slepian-Wolf coding and Wyner-Ziv coding. The Slepian and Wolf coding have theoretically shown that separate encoding is as efficient as joint coding for lossless compression in channel coding.