Adaptive Distributed Source Coding

• Published in: IEEE transactions on image processing Vol. 21; no. 5; pp. 2630 - 2640
• Main Authors: Varodayan, D., Yao-Chung Lin, Girod, B.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Approximation; Codes; Coding; Coding, codes; Complexity theory; Data Compression - methods; Decoders; Decoding; Density; Detection, estimation, filtering, equalization, prediction; Doping; Entropy; Exact sciences and technology; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image processing; Information, signal and communications theory; Mathematical models; Monitoring; Monitoring systems; Parity check codes; Photography - methods; Reproducibility of Results; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal Processing, Computer-Assisted; Signal, noise; Source coding; Studies; sum product algorithm; Telecommunications and information theory; Vectors; Video Recording - methods; video signal processing; Performance evaluation; Source coding; Coding circuit; Doping; Distributed source signal; Information rate; Video technique; Decoding; Algorithm; Information transmission; Video signal processing; Remote sensing; Adaptive coding; Image quality; Monitoring system; Quality control; sum product algorithm; Remote supervision
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2011.2175936

We consider distributed source coding in the presence of hidden variables that parameterize the statistical dependence among sources. We derive the Slepian-Wolf bound and devise coding algorithms for a block-candidate model of this problem. The encoder sends, in addition to syndrome bits, a portion of the source to the decoder uncoded as doping bits. The decoder uses the sum-product algorithm to simultaneously recover the source symbols and the hidden statistical dependence variables. We also develop novel techniques based on density evolution (DE) to analyze the coding algorithms. We experimentally confirm that our DE analysis closely approximates practical performance. This result allows us to efficiently optimize parameters of the algorithms. In particular, we show that the system performs close to the Slepian-Wolf bound when an appropriate doping rate is selected. We then apply our coding and analysis techniques to a reduced-reference video quality monitoring system and show a bit rate saving of about 75% compared with fixed-length coding.