Open AccessVQ‐based compressive sensing with high compression quality
Author(s) -
Fan Haiju,
Li Ming,
Mao Wentao
Publication year - 2017
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.1321
Subject(s) - compressed sensing , algorithm , computer science , matrix (chemical analysis) , compression ratio , compression (physics) , signal to noise ratio (imaging) , image compression , pattern recognition (psychology) , peak signal to noise ratio , image (mathematics) , mathematics , artificial intelligence , image processing , telecommunications , physics , composite material , thermodynamics , internal combustion engine , materials science
Natural image reconstruction based on compressive sensing (CS) has shown a promising performance in recent years. However, sometimes the restoration precision is not high enough. A novel CS algorithm using vector quantisation (VQ) error is proposed. First, the original image is compressed by VQ due to its extremely high compression ratio and strong ability to preserve details. Then compute the VQ error matrix and ignore the three least significant bits, which makes the error matrix much sparser. Next, to ensure a uniform distribution of sparsity of blocks, the error matrix is scrambled. Since the huge diversity among blocks has been largely reduced, they can be sensed with the same sensing matrix in space domain. At last, the reconstruction effect of the error matrix decides the total restoration performance. Experimental results have demonstrated the proposed method, at low measurement ratio, performs better in the aspects of perception and peak signal‐to‐noise ratio.