1. Efficient image structural similarity quality assessment method using image regularised feature
- Author
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Pengfei Zhang, Huan Yang, Yajing Li, Guojia Hou, Baoxiang Huang, and Jinming Duan
- Subjects
Image quality ,Computer science ,business.industry ,media_common.quotation_subject ,Feature extraction ,020206 networking & telecommunications ,Pattern recognition ,Image processing ,02 engineering and technology ,Luminance ,Image texture ,Feature (computer vision) ,Signal Processing ,0202 electrical engineering, electronic engineering, information engineering ,Contrast (vision) ,020201 artificial intelligence & image processing ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Image resolution ,Software ,media_common - Abstract
Image regularised features play a critical role in image processing domain, by integrating regularised feature and structural similarity, a new full-reference image assessment method (IRF_SSIM) is proposed in this study. As well known, the gradient operator always be used to capture the edge information of the image, while the total variational regularised features can be adopted to calculate the detailed change information of image contrast and texture, as well as noise removal and edge retention. Therefore, the IRF_SSIM method extends the gradient features into the image regularised features to measure the structural changes in the image. In addition, image quality is also affected by variations of luminance and contrast. For a more comprehensive image quality assessment, the IRF_SSIM method considers the changes in structure, luminance and contrast simultaneously. In other words, the total image quality is estimated by structural similarity calculated by integrating the effects of image structure, luminance and contrast changes. Comparing with the representative methods, the experimental results illustrate that the IRF_SSIM method is highly consistent with the subjective assessment results.
- Published
- 2020
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