Fovea Based Image Quality Assessment

Humans are the ultimate receivers of the visual information contained in an image, so the reasonable method of image quality assessment (IQA) should follow the properties of the human visual system (HVS). In recent years, IQA methods based on HVS-models are slowly replacing classical schemes, such as mean squared error (MSE) and Peak Signal-to-Noise Ratio (PSNR). IQA-structural similarity (SSIM) regarded as one of the most popular HVS-based methods of full reference IQA has apparent improvements in performance compared with traditional metrics in nature, however, it performs not very well when the images' structure is destroyed seriously or masked by noise. In this paper, a new efficient fovea based structure similarity image quality assessment (FSSIM) is proposed. It enlarges the distortions in the concerned positions adaptively and changes the importances of the three components in SSIM. FSSIM predicts the quality of an image through three steps. First, it computes the luminance, contrast and structure comparison terms; second, it computes the saliency map by extracting the fovea information from the reference image with the features of HVS; third, it pools the above three terms according to the processed saliency map. Finally, a commonly experimental database LIVE IQA is used for evaluating the performance of the FSSIM. Experimental results indicate that the consistency and relevance between FSSIM and mean opinion score (MOS) are both better than SSIM and PSNR clearly.