Adaptive Tchebichef Moment Transform Image Compression using Psychovisual Model

Abstract

An extension of the standard JPEG image compression known as JPEG-3 allows rescaling of the quantization matrix to achieve a certain image output quality. Recently, Tchebichef Moment Transform (TMT) has been introduced in the field of image compression. TMT has been shown to perform better than the standard JPEG image compression. This study presents an adaptive TMT image compression. This task is obtained by generating custom quantization tables for low, medium and high image output quality levels based on a psychovisual model. A psychovisual model is developed to approximate visual threshold on Tchebichef moment from image reconstruction error. The contribution of each moment will be investigated and analyzed in a quantitative experiment. The sensitivity of TMT basis functions can be measured by evaluating their contributions to image reconstruction for each moment order. The psychovisual threshold model allows a developer to design several custom TMT quantization tables for a user to choose from according to his or her target output preference. Consequently, these quantization tables produce lower average bit length of Huffman code while still retaining higher image quality than the extended JPEG scaling scheme.