Compressive sensing with adaptive pixel domain reconstruction for block-based video coding

This paper presents a new look at image/video compression from the compressive sensing's perspective. Quantization in video compression can be regarded as a subsampling process where the signal is mapped into predefined levels. We view the problem of signal reconstruction from its quantized sig...

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Bibliographic Details
Published in2010 IEEE International Conference on Image Processing pp. 3377 - 3380
Main Authors Do, T T, Xiaoan Lu, Sole, J
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2010
Subjects
compressive sensing
Encoding
Image reconstruction
Noise
nonlinear reconstruction
Pixel
Quantization
Sensors
Total-Variation minimization
Transforms
Video coding
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Summary:This paper presents a new look at image/video compression from the compressive sensing's perspective. Quantization in video compression can be regarded as a subsampling process where the signal is mapped into predefined levels. We view the problem of signal reconstruction from its quantized signal vector as a compressive sensing recovery problem where the quantized coefficients are subsampled measurements. Based on this observation, we propose a novel method of image/video coding that employs an adaptive Total-Variation (TV) minimization in the pixel domain to recover the gradient-sparse image blocks from their quantized transform coefficients. We further increase the coding efficiency by encoding only a subset of the transform coefficients and discard the remaining ones. Experiment results show that the proposed framework is efficient with gradient-sparse video signals and outperform the video compression standard H.264/AVC by up to 7% of bitrate reduction.
ISBN:9781424479924
1424479924
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2010.5652726