No-Reference Perceptual Quality Assessment of JPEG Compressed Images

This research aims to develop no-reference quality measurement algorithms for JPEG compressed images. See paper.

First, we established a JPEG image database and subjective experiments were conducted on the database. There are 120 test images in the database. 30 of them are original images (shown below). The rest are JPEG-compressed. 53 subjects were shown the database; most of them were the students taking the Digital Image and Video Processing course in the Fall 2001 semester in the Dept. of ECE, The Univ. of Texas at Austin. The subjects were asked to assign each image a quality score between 1 and 10 (10 represents the best quality and 1 the worst). The 53 scores of each image were averaged to a final Mean Opinion Score (MOS) of the image.

 

Second, we show that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Below is the PSNR results versus MOS.

Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance as shown below.

A Matlab implementation of the proposed method is available here. You can download it for free, change it as you like and use it anywhere, but please refer to its original source (cite our paper and this web page).

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