Adaptive quantization of picture signals using spatial masking

Visual sensitivity of human observers decreases at and adjacent to large luminance changes: a fact well known to psychophysicists but not yet fully utilized for picture coding. In this paper we present a systematic investigation of these changes in visual sensitivity and apply it to adapt the quantizer of a predictive coder. The paper consists of three parts. In the first part, earlier psychophysical work in related areas relevant to picture coding is briefly reviewed. Certain simple measures of luminance activity (called the masking functions) are constructed. Subjective experiments which obtain fidelity measures related to the masking functions, using complex scenes (real-life pictures, head and shoulders view), are described. These relationships, called the visibility functions, express relationship between the relative amplitude accuracy required by the viewer and the masking functions. Perceptual, statistical, and contextual properties are inherent in them. Relationship between the visibility functions and some earlier measurements of psychovisual weighting functions are pointed out. In the second part, several adaptation strategies for the quantizer of a predictive DPCM coder are discussed. These include uniform as well as nonuniform quantizers with or without entropy constraints. These strategies are simulated on a computer, and the results are presented in the third part. The simulations indicate that, for the same picture quality, by using adaptive strategies, entropy reductions of about 30-50 percent are possible over nonadaptive techniques.