Paper
14 November 2007 Image quality evaluation method based on structural similarity
Li Zhu, Guoyou Wang, Ying Liu
Author Affiliations +
Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 67905L (2007) https://doi.org/10.1117/12.774817
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
Aiming at solving the limit of current distortion sensitivity analysis(HVS is a complicated non-linear system, while the vision models current are linear and simple), we research a new image quality evaluation method based on structural similarity, that is, to get a general similarity from luminance, contrast and image construction, as an objective quality evaluation criteria. In this way, the method fully considers both image structure information and human vision characteristics. Based on human visual comprehension of image content, the method evaluates the subjective human visual perception to image quality by arithmetic modeling, so it ensures the structural similarity model matches the application purpose of image processing. After theory deduction and algorithm validation, the method provides reasons to select a proper image compression algorithm and gives a way to evaluate image quality efficiently. Experiments show that, to evaluate reconstructed images encoded by compression algorithm Set Partitioning in Hierarchical Trees (SPIHT), compared with the traditional evaluation method based on Peak Signal-to-Noise Ratio (PSNR), the method proposed in this paper is more effective to the perception of people's eyes.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Li Zhu, Guoyou Wang, and Ying Liu "Image quality evaluation method based on structural similarity", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67905L (14 November 2007); https://doi.org/10.1117/12.774817
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Cited by 1 scholarly publication.
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KEYWORDS
Image quality

Image compression

Visual process modeling

Visualization

Distortion

Image processing

Image quality standards

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