Haar image compression using a neural network
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Image compression using neural networks and haar wavelet
WSEAS Transactions on Signal Processing
A natural image quality evaluation metric
Signal Processing
Locally Adaptive Perceptual Compression for Color Images
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Wavelet based color image compression using vector quantization and morphology
Proceedings of the International Conference on Advances in Computing, Communication and Control
Image quality assessment based on multiscale geometric analysis
IEEE Transactions on Image Processing
Reduced-reference IQA in contourlet domain
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Digital removal of blotches with variable semi-transparency using visibility laws
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
An HVS based adaptive quantization scheme for the compression of color images
Digital Signal Processing
Wavelet-based directional structural distortion model for image quality assessment
Pattern Recognition and Image Analysis
A perceptually tuned watermarking scheme for color images
IEEE Transactions on Image Processing
Perceptually-based compensation of light pollution in display systems
Proceedings of the ACM SIGGRAPH Symposium on Applied Perception in Graphics and Visualization
Color image compression: early vision and the multiresolution representations
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Multiresolution, perceptual and vector quantization based video codec
Multimedia Tools and Applications
Objective and subjective evaluation of static 3D mesh compression
Image Communication
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The visual efficiency of an image compression technique depends directly on the amount of visually significant information it retains. By "visually significant" we mean information to which a human observer is most sensitive. The overall sensitivity depends on aspects such as contrast, color, spatial frequency, and so forth. One important aspect is the inverse relationship between contrast sensitivity and spatial frequency. This is described by the contrast sensitivity function (CSF). In compression algorithms the CSF can be exploited to regulate the quantization step-size to minimize the visibility of compression artifacts. Existing CSF implementations for wavelet-based image compression use the same quantization step-size for a large range of spatial frequencies. This is a coarse approximation of the CSF. This paper presents two new techniques that implement the CSF at significantly higher precision, adapting even to local variations of the spatial frequencies within a decomposition subband. The approaches can be used for luminance as well as color images. For color perception three different CSFs describe the sensitivity. The implementation technique is the same for each color band. Implemented into the JPEG2000 compression standard, the new techniques are compared to conventional CSF-schemes. The proposed techniques turn out to be visually more efficient than previously published methods. However, the emphasis of this paper is on how the CSF can be implemented in a precise and locally adaptive way, and not on the superior performance of these techniques.