Improved estimation for just-noticeable visual distortion
Signal Processing
A perceptually optimized JPEG-LS coder for color images
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
No-reference noticeable blockiness estimation in images
Image Communication
Locally Adaptive Perceptual Compression for Color Images
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A patch-based structural masking model with an application to compression
Journal on Image and Video Processing - Special issue on patches in vision
Perceptual optimization for scalable video compression based on visual masking principles
IEEE Transactions on Circuits and Systems for Video Technology
A perceptually optimized JPEG-LS coder for color images
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
An HVS based adaptive quantization scheme for the compression of color images
Digital Signal Processing
Multiresolution HVS and statistically based image coding scheme
Multimedia Tools and Applications
A perceptually tuned watermarking scheme for color images
IEEE Transactions on Image Processing
Perceptually tuned subband coder for JPEG
Journal of Real-Time Image Processing
Novel adaptive color space transform and application to image compression
Image Communication
Inpainting with image patches for compression
Journal of Visual Communication and Image Representation
A visual model for estimating the perceptual redundancy inherent in color images
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Multiresolution, perceptual and vector quantization based video codec
Multimedia Tools and Applications
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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