Natural image compression using an extended non-negative sparse coding neural network technique

This paper proposes an extended non-negative sparse coding (NNSC) neural network method for image compression. This method can exploit the NNSC algorithm to obtain transform-based compression schemes adapted to standard natural image classes, which results from the statistical properties of natural image data. In particular, several methods of image compression such as linear principal component analysis (PCA), wavelet-based analysis, independent component analysis (ICA), etc., are evaluated and compared based on both the standard signal to noise ratio (SNR) and picture quality scale (PQS) criteria. The simulation results show that, in the case of using a fixed block by block scanning a natural image randomly, the quality of a compressed image obtained by our extended NNSC compression algorithm indeed outperforms the one obtained by other algorithms mentioned above.