On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
On learning with dissimilarity functions
Proceedings of the 24th international conference on Machine learning
On the performance evaluation of 3D reconstruction techniques from a sequence of images
EURASIP Journal on Applied Signal Processing
Theory and algorithm for learning with dissimilarity functions
Neural Computation
RDSR-V. Reliable Dynamic Source Routing for video-streaming over mobile ad hoc networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Temporal error concealment algorithm using fuzzy metric
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
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Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L2-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with the human visual perception in many cases. A fuzzy image metric (FIM) is defined based on Sugeno's (1977) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgment procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using the PSNR. Compared to the L2-metric, we demonstrate that a better performance can be achieved in fractal coding by using the proposed FIM