Image Coding For Data Compression Using A Human Visual Model

Abstract

We show that when mean -square error is used to determine the performance of image compression algorithms, in particular vector quantization algorithms, the mean -square error measurement is dependent upon the data type of the digitized images. When using vector quantization the possibility exists for encoding images of one type with code books of another type, we show that this cross -encoding has an adverse effect on performance. Thus, when making comparative evaluations of different vector quantization compression techniques one must be careful to document the data type used in both the code book and the test image data. We also show that when mean -square error measurements are made in the perceptual space of a human visual model, the distortion measurements correlate more with subjective image evaluation than when the distortions are calculated in other spaces. We use a monochrome visual model to improve the quality of vector quantized images, but our preliminary results indicate that in general, the performance of the model is dependent upon the type of data and the coding method used.