H
Hamid R. Sheikh
Researcher at Samsung
Publications - 71
Citations - 54048
Hamid R. Sheikh is an academic researcher from Samsung. The author has contributed to research in topics: Image quality & Image processing. The author has an hindex of 21, co-authored 70 publications receiving 42234 citations. Previous affiliations of Hamid R. Sheikh include Texas Instruments & University of Texas at Dallas.
Papers
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Image information and visual quality
Hamid R. Sheikh,Alan C. Bovik +1 more
TL;DR: An image information measure is proposed that quantifies the information that is present in the reference image and how much of this reference information can be extracted from the distorted image and combined these two quantities form a visual information fidelity measure for image QA.
Journal ArticleDOI
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
TL;DR: This paper presents results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects and is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image.
Proceedings ArticleDOI
Image information and visual quality
Hamid R. Sheikh,Alan C. Bovik +1 more
TL;DR: This work proposes an information fidelity criterion that quantifies the Shannon information that is shared between the reference and distorted images relative to the information contained in the reference image itself, and demonstrates the performance of the algorithm by testing it on a data set of 779 images.
Journal ArticleDOI
An information fidelity criterion for image quality assessment using natural scene statistics
TL;DR: This paper proposes a novel information fidelity criterion that is based on natural scene statistics and derives a novel QA algorithm that provides clear advantages over the traditional approaches and outperforms current methods in testing.