DocumentCode :
1373354
Title :
Locally adaptive perceptual image coding
Author :
Höntsch, Ingo ; Karam, Lina J.
Author_Institution :
Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA
Volume :
9
Issue :
9
fYear :
2000
fDate :
9/1/2000 12:00:00 AM
Firstpage :
1472
Lastpage :
1483
Abstract :
Most existing efforts in image and video compression have focused on developing methods to minimize not perceptual but rather mathematically tractable, easy to measure, distortion metrics. While nonperceptual distortion measures were found to be reasonably reliable for higher bit rates (high-quality applications), they do not correlate well with the perceived quality at lower bit rates and they fail to guarantee preservation of important perceptual qualities in the reconstructed images despite the potential for a good signal-to-noise ratio (SNR). 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 is based on a locally adaptive perceptual quantization scheme for compressing the visual data. Our strategy is to exploit human visual masking properties by deriving visual masking thresholds in a locally adaptive fashion based on a subband decomposition. The derived masking thresholds are used in controlling the quantization stage by adapting the quantizer reconstruction levels to the local amount of masking present at the level of each subband transform coefficient. Compared to the existing non-locally adaptive perceptual quantization methods, the new locally adaptive algorithm exhibits superior performance and does not require additional side information. This is accomplished by estimating the amount of available masking from the already quantized data and linear prediction of the coefficient under consideration. By virtue of the local adaptation, the proposed quantization scheme is able to remove a large amount of perceptually redundant information. Since the algorithm does not require additional side information, it yields a low entropy representation of the image and is well suited for perceptually lossless image compression
Keywords :
adaptive codes; entropy codes; image coding; image reconstruction; image representation; prediction theory; quantisation (signal); rate distortion theory; transform coding; visual perception; SNR; bit rate; distortion metrics minimisation; entropy coding; high-quality applications; human visual masking properties; image components; image compression; image quality; linear prediction; locally adaptive algorithm; locally adaptive perceptual image coding; locally adaptive perceptual quantization; low entropy image representation; nonperceptual distortion measures; perceived quality; perceptual relevance; perceptual-based image coder; perceptually lossless image compression; quantizer reconstruction; reconstructed image; signal-to-noise ratio; subband decomposition; subband transform coefficient; video compression; visual data compression; visual masking thresholds; Adaptive algorithm; Bit rate; Distortion measurement; Humans; Image coding; Image reconstruction; Masking threshold; Quantization; Signal to noise ratio; Video compression;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/83.862622
Filename :
862622
Link To Document :
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