Locally adaptive perceptual image coding

Most existing efforts in image and video compression have focused on develo ping methods to minimize not perceptual but rather mathematically tractable , easy to measure, distortion metrics, While nonperceptual distortion measu res were found to be reasonably reliable for higher bit rates (high-quality applications), they do not correlate well with the perceived quality at lo wer bit rates and they fail to guarantee preservation of important perceptu al qualities in the reconstructed images despite the potential for a good s ignal-to-noise ratio (SNR). This paper presents a perceptual-based image co der, which discriminates between image components based on their perceptual relevance for achieving increased performance in terms of quality and bit rate. The new coder is based on a locally adaptive perceptual quantization scheme for compressing the visual data. Our strategy is to exploit human vi sual masking properties by deriving visual masking thresholds in a locally adaptive fashion based on a subband decomposition. The derived masking thre sholds are used in controlling the quantization stage by adapting the quant izer reconstruction levels to the local amount of masking present at the le vel of each subband transform coefficient. Compared to the existing non Loc ally adaptive perceptual quantization methods, the new locally adaptive alg orithm exhibits superior performance and does not require additional side i nformation. This is accomplished by estimating the amount of available mask ing from the already quantized data and linear prediction of the coefficien t under consideration, By virtue of the local adaptation, the proposed quan tization scheme is able to remove a large amount of perceptually redundant information. Since the algorithm does not require additional side informati on, it yields a low entropy representation of the image and is well suited for perceptually lossless image compression.