Texture analysis assessment for images

Abstract

Commonly, the existing metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR), quality index (QI), structural similarity index metric (SSIM), and quality index based on local variance (QILV) use the image intensity-based statistics approach to assess the quality of distorted images. These metrics are successful in discriminating the quality of distorted images, such as de-noising, JPEG compressed, and blur images. However, they are unsuccessful in discriminating the quality of channel decomposition images. Therefore, this paper proposes the texture analysis assessment (TAA) to measure the quality of both normally distorted images and channel decomposition images. The proposed metric uses image intensity statistics in conjunction with texture analysis for quality discrimination of slightly different distorted and channel decomposition images. The texture analysis based on edge orientation is an important part employed to measure precise image errors. The experimental results illustrate that the TAA metric can evidently discriminate the quality of normally distorted images and channel decomposition images, when compared with state-of-the-art metrics. Furthermore, the perceived visual quality and the quality value of TAA are corresponding; the lower visual quality human-eye perceives, the lower quality value TAA measures, and vice versa.