Paper No: 132
Image Compression by Redundancy Reduction
Author(s): Carlos Sousa, Andre Cavalcante, Denner Guilhon, Allan Barros
Image compression is achieved by reducing redundancy between neighboring pixels
but preserving features such as edges and contours of the original image.
Deterministic and statistical models are
usually employed to reduce redundancy. Compression methods that use
statistics have heavily been in°uenced by neuroscience research. In this
work, we propose an image compression system based on the efficient coding concept
derived from neural information processing models. The system performance is
compared with principal component analysis (PCA)
and the discrete cosine transform (DCT) at several compression ratios (CR). Evaluation
through both visual inspection and objective measurements showed that the proposed
system is more robust to distortions
such as ringing and block artifacts than PCA and DCT.