Emphatic visual speech synthesis
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
Fuzzy Sets and Systems
A new perceptual organization approach to 3D measuring system based on the fuzzy integral
Image and Vision Computing
Multi-sensor data fusion based on fuzzy integral in AR system
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
A content-based image quality metric
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Video quality assessment combining structural distortion and human visual system
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Fuzzy logic and temporal information applied to video quality assessment
Journal of Mobile Multimedia
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Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.