A Frequency Sensitivity-Based Quality Prediction Model for JPEG Images

A quality prediction model for images coded with JPEG is proposed in this paper. This model estimates the quality of an image at a given compressed ratio based on the structural similarity theory, without actual coding of the image. As different frequencies play various roles in human vision, the frequency sensitivity-based structural similarity model is introduced in this paper. The proposed model has a better correlation with the subjective judgment of human observers than both commonly used PSNR and newly proposed SSIM, because it emphasizes more on human eye's sensitive frequency bands. Experimental results with real images also show that the prediction error is less than 0.1 structural similarity index for over 80% test images.