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doi:10.1016/j.jvcir.2005.11.002 
Copyright © 2005 Elsevier Inc. All rights reserved.
Perceptual quality and objective quality measurements of compressed videos
Ee Ping Onga, 
, 
, Xiaokang Yanga, Weisi Lina, Zhongkang Lua, Susu Yaoa, Xiao Lina, Susanto Rahardjaa and Boon Choong Sengb
Received 30 April 2004;
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Abstract
This paper firstly presents a study of video quality for H.264 compressed videos compared to MPEG-4 (simple profile) from a perceptual point of view. Traditionally, peak signal-to-noise ratio (PSNR) has been used to represent the quality of a compressed video sequence. However, PSNR has been found to correlate poorly with subjective quality ratings, particularly at much lower bitrates and low frame rates. Thus, an alternative that has been commonly used is to perform subjective test where a large number of human subjects are used to gauge the quality of a video. However, this process is not only time-consuming but also tedious and expensive to perform. Hence, this paper further proposes an objective video quality measurement method to automatically measure the perceptual quality of a stream of video images. The proposed method has been tested on multimedia videos, consisting of CIF and QCIF video sequences compressed at various bitrates (24–384 kbps) and frame rates (7.5–30 fps) using the H.264 and MPEG-4 video codecs and it is shown to give significantly better correlations to the human perception than PSNR and the video structural similarity method.
Keywords: H.264; MPEG-4; Video coding; Perceptual quality; Objective video quality measurement; Full-reference; Subjective quality ratings
Article Outline
- 1. Introduction
- 2. Subjective quality assessment
- 3. Objective metric performance measures
- 4. Comparison of perceptual quality of H.264 and MPEG4 compressed videos
- 5. Proposed video quality measurement method
- 5.1. Video quality metric
- 5.2. Content richness fidelity
- 5.3. Block-fidelity
- 5.4. Distortion-invisibility
- 5.4.1. Visibility threshold
- 5.4.2. Temporal masking
- 5.4.3. Luminance masking
- 5.4.4. Spatial-textural masking
- 6. Metric parameterisation
- 7. Results
- 8. Conclusions
- References
