Locally-adaptive image coding based on a perceptual target distortion

This paper presents a perceptual-based image coder, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit-rate. The new coder uses a locally-adaptive perceptual quantization scheme based on a tractable perceptual distortion metric. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally-adaptive fashion. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels in order to meet a desired target perceptual distortion. The proposed coding scheme is flexible in that it works with any subband-based decomposition and with block-based transform methods. Compared to the existing perceptual transform-based and block-based methods, the proposed perceptual coding method exhibits superior performance in terms of bit rate and distortion control. Coding results are presented to illustrate the performance of the presented coding scheme.