Motion-Aware Decoding of Compressed-Sensed Video

• Published in: IEEE transactions on circuits and systems for video technology Vol. 23; no. 3; pp. 438 - 444
• Main Authors: Liu, Ying, Li, Ming, Pados, Dimitris A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2013; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Coding, codes; Compressed sensing; compressive sampling; Correlation; Decoding; dimensionality reduction; Discrete cosine transforms; Exact sciences and technology; Image processing; Image reconstruction; Information, signal and communications theory; Iterative decoding; motion estimation; Radiocommunications; Sampling, quantization; Signal and communications theory; Signal processing; sparse representation; Streaming media; Telecommunications; Telecommunications and information theory; Transmitters. Receivers; Vectors; video codecs; video streaming; Video coding; Streaming; Karhunen Loeve transformation; Motion estimation; Image processing; Data compression; compressive sampling; Video signal; Receiver; Decoding circuit; Image compression; Decoding; Video codecs; Adaptive method; Information transmission; Video signal processing; Dimension reduction; Image coding; dimensionality reduction; Image sequence; Signal processing; Sparse representation; video streaming; Compressed sensing
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2012.2207269

Compressed sensing is the theory and practice of sub-Nyquist sampling of sparse signals of interest. Perfect reconstruction may then be possible with much fewer than the Nyquist required number of data. In this paper, in particular, we consider a video system where acquisition is carried out in the form of direct compressive sampling (CS) with no other form of sophisticated encoding. Therefore, the burden of quality video sequence reconstruction falls solely on the receiver side. We show that effective implicit motion estimation and decoding can be carried out at the receiver or decoder side via sparsity-aware recovery. The receiver performs sliding-window interframe decoding that adaptively estimates Karhunen-Loève bases from adjacent previously reconstructed frames to enhance the sparse representation of each video frame block, such that the overall reconstruction quality is improved at any given fixed CS rate. Experimental results included in this paper illustrate the presented developments.