No-reference image quality assessment based on neighborhood co-occurrence matrix
| Autor: | Wen Lu, Weiquan He, Ziheng Zhou, Jiachen Yang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Spatial correlation
Pixel Computer science Image quality business.industry media_common.quotation_subject Scene statistics 020206 networking & telecommunications Pattern recognition 02 engineering and technology Co-occurrence matrix Signal Processing Human visual system model 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 Focus (optics) business Software media_common |
| Zdroj: | Signal Processing: Image Communication. 81:115680 |
| ISSN: | 0923-5965 |
| Popis: | No-reference image quality assessment (NR-IQA) aims to develop models that can predict the quality of distorted image automatically and accurately in the absent of reference image. Previous NR-IQA methods based on natural scene statistics (NSS) always focus on the luminance contrast of image but attach limited attention to pixel-wise relationship. However, human visual system (HVS) is highly adaptive to extract spatial correlation according to relative position within visual field. In this paper, a new approach is proposed for NR-IQA, in which the neighborhood co-occurrence matrix (NCM) is introduced to describe spatial correlation of pixels for quality assessment. The NCM is constructed based on spatial correlation of every pixel and its neighborhood through a mapping to highlight the one-to-many pixel-wise relationship. Moreover, a series of tailored statistical metrics are designed to quantify the unnaturalness extent of NCM effectively, which is combined with others natural scene statistics to predict image quality. Extensive experiments demonstrate the proposed method has superior performance against compared methods, and achieves significant improvements on distortions associated with color or locality. |
| Databáze: | OpenAIRE |
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