An Approach to Image Compression by Using Sparse Approximation Technique

• Published in: Procedia computer science Vol. 48; pp. 769 - 775
• Main Authors: Pati, Nibedita, Pradhan, Annapurna, Kanoje, Lalit Kumar, Das, Tanmaya Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2015
• Subjects: Discrete Cosine Transform; Orthogonal Matching Pursuit; Over-complete dictionary; Sparse Coding; Sparse Coding; Over-complete dictionary; Discrete Cosine Transform; Orthogonal Matching Pursuit
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2015.04.213

Image compression has been a widely researched field for decades. Recently, there has been a growing interest in using basis selection algorithms for signal approximation and compression. Signal approximation using a linear combination of basis from an over-complete dictionary has proven to be an NP-hard problem. By selecting a smaller number of basis than the span of the signal, we achieve glossy compression in exchange for a small reconstruction error. For the past few decades orthogonal and bi-orthogonal complete dictionaries such as Discrete Cosine Transform (DCT) or wavelets were the dominant transform domain representations of signals. Over-complete dictionaries have been intensively studied and successfully applied to various application such as image de-noising, compression etc. The DCT and wavelet based compression methods suffer from blocking and ringing artefacts and also they are unable to capture the directional information. So, in this context an investigation has been made by using sparse coding method by Orthogonal Matching Pursuit (OMP) algorithm. In this proposed work, conventional DCT can be replaced by a set of trained dictionaries. For dictionary construction we have used a combination of DCT and Gabor basis and to encode the trained dictionary elements we have employed OMP algorithm. Experimental results demonstrate that the proposed method provides gains in Rate Distortion (RD) performance and improvements in perceptual quality.