The cortex transform: rapid computation of simulated neural images
Computer Vision, Graphics, and Image Processing
Lineal Feature Extraction by Parallel Stick Growing
IRREGULAR '96 Proceedings of the Third International Workshop on Parallel Algorithms for Irregularly Structured Problems
Image coding in the context of a psychovisual image representation with vector quantization
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Subjective and objective quality evaluation of LAR coded art images
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Blind DT-CWT domain additive spread-spectrum watermark detection
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
A full-reference quality metric for geometrically distorted images
IEEE Transactions on Image Processing
Structural similarity image quality reliability
Signal Processing
Automatic prediction of perceptual quality of multimedia signals--a survey
Multimedia Tools and Applications
Image quality assessment based on edge preservation
Image Communication
Image noise detection in global illumination methods based on fast relevance vector machine
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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When an image is supposed to have been transformed by a process like image enhancement or lossy image compression for storing or transmission, it is often necessary to measure the quality of the distorted image. This can be achieved using an image processing method called ''quality criterion''. Such a process must produce objective quality scores in close relationship with subjective quality scores given by human observers during subjective quality assessment tests. In this paper, an image quality criterion is proposed. This criterion, called C4, is fully generic (i.e., not designed for predefined distortion types or for particular images types) and based on a rather elaborate model of the human visual system (HVS). This model describes the organization and operation of many stages of vision, from the eye to the ventral and dorsal pathways in the visual cortex. The novelty of this quality criterion relies on the extraction, from an image represented in a perceptual space, of visual features that can be compared to those used by the HVS. Then a similarity metric computes the objective quality score of a distorted image by comparing the features extracted from this image to features extracted from its reference image (i.e., not distorted). Results show a high correlation between produced objective quality scores and subjective ones, even for images that have been distorted through several different distortion processes. To illustrate these performances, they have been computed using three different databases that employed different contents, distortions type, displays, viewing conditions and subjective protocols. The features extracted from the reference image constitute a reduced reference which, in a transmission context with data compression, can be computed at the sender side and transmitted in addition to the compressed image data so that the quality of the decompressed image can be objectively assessed at the receiver side. More, the size of the reduced reference is flexible. This work has been integrated into freely available applications in order to formulate a practical alternative to the PSNR criterion which is still too often used despite its low correlation with human judgments. These applications also enable quality assessment for image transmission purposes.