Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation

Jin XU  Yan ZHANG  Zhizhong FU  Ning ZHOU  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.4   pp.918-922
Publication Date: 2017/04/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
Keyword: 
distributed compressive video sensing,  perceptual coding,  reweighted sampling,  rate-distortion optimized measurements allocation,  

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Summary: 
Distributed compressive video sensing (DCVS) is a new paradigm for low-complexity video compression. To achieve the highest possible perceptual coding performance under the measurements budget constraint, we propose a perceptual optimized DCVS codec by jointly exploiting the reweighted sampling and rate-distortion optimized measurements allocation technologies. A visual saliency modulated just-noticeable distortion (VS-JND) profile is first developed based on the side information (SI) at the decoder side. Then the estimated correlation noise (CN) between each non-key frame and its SI is suppressed by the VS-JND. Subsequently, the suppressed CN is utilized to determine the weighting matrix for the reweighted sampling as well as to design a perceptual rate-distortion optimization model to calculate the optimal measurements allocation for each non-key frame. Experimental results indicate that the proposed DCVS codec outperforms the other existing DCVS codecs in term of both the objective and subjective performance.