Compressed Image Quality Assessment:A Metric Based on Weighted Structural Similirity
Fu Ji · Computer Knowledge and Technology · 2015
Image quality assessment(IQA) is an important area of computer vision in recent years. In the process of delivery and storage, objective quality assessment is essential for compressed images. Although the calculation of Mean Square Error(MSE)and Peak Signal to Noise Ratio(PSNR) are simple, their reflection to the perceptual quality of compressed images are not accurate. Recently, in order to improve the IQA ability, many excellent IQA methods are developed, such as SSIM, IFC, VSNR, etc.There are still a large ascension for compressed images although these metrics have good performances. As we known, the high frequency signals are more seriously distorted because of the DCT transform in image compression. Thus, based on the idea that the part of serious distortion can better present the degeneration of original images, we adopt the method as weighted structural similarity to evaluate the quality of compressed images. Extensive experiments have been performed on four benchmark databases, which demonstrate that the proposed method is more effective than a number of state-of-the-art IQA metrics.