Vector quantization of images using visual masking functions

ABSTRACT This paper presents an image compression technique that incorporates visual masking functions in vector quantizer systems. Visual masking functions provide a description of the maximum amount of noise that can be present in an image, while remaining undetected when the image is viewed by an observer. The basic idea employed in this work is that of a spatially varying distortion measure which is defined to be zero where the error involved is below a threshold level defined by the visual masking function. A gradient based algorithm is used to generate the vector quantizer codebooks. Experimental results involving subband vector quantization and a perceptual masking function recently proposed by Safranek and Johnston are presented in this paper.