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.