No-Reference Perceptual Quality Assessment of JPEG
Compressed Images
This
research aims to develop a no-reference quality measure for JPEG compressed
images. See
paper. Download algorithm.
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).
See paper
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Last
updated Jan. 18, 2002
