Content-aware video quality assessment: predicting human perception of quality using peak signal to noise ratio and spatial/temporal activity

B. Ortiz-Jaramillo; J. Niño-Castañeda; Ljiljana Platiša; W. Philips

doi:10.1117/12.2083026

16 March 2015 Content-aware video quality assessment: predicting human perception of quality using peak signal to noise ratio and spatial/temporal activity

B. Ortiz-Jaramillo, J. Niño-Castañeda, Ljiljana Platiša, W. Philips

Author Affiliations +

Proceedings Volume 9399, Image Processing: Algorithms and Systems XIII; 939917 (2015) https://doi.org/10.1117/12.2083026
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

Since the end-user of video-based systems is often a human observer, prediction of human perception of quality (HPoQ) is an important task for increasing the user satisfaction. Despite the large variety of objective video quality measures, one problem is the lack of generalizability. This is mainly due to the strong dependency between HPoQ and video content. Although this problem is well-known, few existing methods directly account for the influence of video content on HPoQ.

This paper propose a new method to predict HPoQ by using simple distortion measures and introducing video content features in their computation. Our methodology is based on analyzing the level of spatio-temporal activity and combining HPoQ content related parameters with simple distortion measures. Our results show that even very simple distortion measures such as PSNR and simple spatio-temporal activity measures lead to good results. Results over four different public video quality databases show that the proposed methodology, while faster and simpler, is competitive with current state-of-the-art methods, i.e., correlations between objective and subjective assessment higher than 80% and it is only two times slower than PSNR.

Citation Download Citation

B. Ortiz-Jaramillo, J. Niño-Castañeda, Ljiljana Platiša, and W. Philips "Content-aware video quality assessment: predicting human perception of quality using peak signal to noise ratio and spatial/temporal activity", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939917 (16 March 2015); https://doi.org/10.1117/12.2083026

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