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.
In current interframe video compression systems, the encoder performs predictive coding to exploit the similarities of successive frames. The Wyner-Ziv theorem on source coding with side information available only at the decoder suggests that an asymmetric video codec, where individual frames are encoded separately, but decoded conditionally (given temporally adjacent frames) could achieve similar efficiency. We report the first results on a Wyner-Ziv coding scheme for motion video that uses intraframe encoding, but interframe decoding.  We propose a compression scheme for still images, by exploiting the theory of Distributed Coding of correlated multi-sources. Two corrupted versions of an image are encoded separately but decoding jointly. Our approach results in twofold. i) use of decomposition of low-pass wavelet co-efficients for creating the Side Information, and ii) variable-length coset creation by estimating the bit-rate of the cosets on the encoder using the joint distribution statistics of the original image and the side info. In the case of coding for mobile terminals, the proposed codec exploits the channel coding principles in order to have a simple encoder with a low transmission rate and high PSNR. Experimental results are given for lossy encoding case. Following recent theoretical works on successive Wyner-Ziv coding (WZC), we propose a practical layered Wyner-Ziv video coder using the DCT, nested scalar quantization, and irregular LDPC code based Slepian-Wolf coding (or lossless source coding with side information at the decoder). Our main novelty is to use the base layer of a standard scalable video coder (e.g., MPEG-4 H.26L FGS or H.263+) as the decoder side information and perform layered WZC for quality enhancement. Similar to FGS coding, there is no performance difference between layered and monolithic WZC when the enhancement bitstream is generated in our proposed coder. Using an H.26L coded version as the base layer, experiments indicate that WZC gives slightly worse performance than FGS coding when the channel (for both the base and enhancement layers) is noiseless. However, when the channel is noisy, extensive simulations of video transmission over wireless networks conforming to the CDMA2000 1X standard show that H.26L base layer coding plus Wyner-Ziv enhancement layer coding are more robust against channel errors than H.26L FGS coding. These results demonstrate that layered Wyner-Ziv video coding is a promising new technique for video streaming over wireless networks