Block-transform lossy image compression is the most widely-used approach for compressing and storing images or video. A novel algorithm to restore highly compressed images with greater image quality is proposed. Since many block-transform coefficients are reduced to zero after quantization, the compressed image restoration problem can be treated as a sparse reconstruction problem where the original image is reconstructed based on sparse, degraded measurements in the form of highly quantized block-transform coefficients. The sparse reconstruction problem is solved by minimizing a homotopic regularized function, subject to data fidelity in the block-transform domain. Experimental results using compressed natural images at di erent levels of compression show improved performance by using the proposed algorithm compared to other methods.

Date Published: 4 March 2015
PDF: 15 pages
Proc. SPIE 9410, Visual Information Processing and Communication VI, 941005 (4 March 2015); doi: 10.1117/12.2082861

Published in SPIE Proceedings Vol. 9410:
Visual Information Processing and Communication VI
Amir Said; Onur G. Guleryuz; Robert L. Stevenson, Editor(s)