Abstract
Due to the wide spread usage of smart devices, adopting video contents service to the diverse end user’s service environment is an essential process. The heterogeneity of end users’ devices, usually referred as the device fragmentation, requires video transcoding which is a lossy process. Accordingly, the subsequent video quality degrading is inevitable. In such circumstances, minimizing perceptible quality loss of video is a key issue for the video contents service provider. However, the video quality loss caused in the process of transcoding is very difficult to measure. Because the “video quality” is a subjective term, it is almost impossible to estimate before video contents are delivered and actually serviced. To address this issue, many research efforts have been pursued for estimating subjective quality evaluation score using objective quality assessment metric. Structural Similarity (SSIM) is a well-known objective quality assessment method. Based on previous studies, this method has been used as a very effective quality assessment tool in video coding system. In this paper, we propose new video quality assessment metric using intensity variation analysis. The intensity metric-based video quality assessment has a high correlation with the SSIM regardless of the category of video contents, resolutions and even bitrate setting. The proposed method that measures inter-frame intensity variation (IV) is more efficient than SSIM in VBR transcoding system. Our experimental results show that the proposed video quality assessment shows up to 22 times faster than SSIM in the execution time. Ultimately, to take its advantage of the short latency and low execution overhead, IV-based video assessment is applicable to real on-demand transcoding and streaming environments while minimizing video quality degradation of transcoding.
Similar content being viewed by others
References
Stockhammer T (2011) Dynamic adaptive streaming over HTTP–design principles and standards. In: Proceedings of the Second Annual ACM Conference on Multimedia Systems, pp 133–144
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612
Xin J, Lin C-W, Sun M-T (2005) Digital video transcoding. Proc IEEE 93(1):84–97
Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Pearson Prentice Hall, Upper Saddle Rive
Paulikas S (2013) Estimation of degraded video quality of mobile H.264/AVC video streaming. In: Proceeding of EuroCon. IEEE, pp 694–699. https://doi.org/10.1109/EUROCON.2013.6625056
Ries M, Nemethova O, Rupp M (2008) Video quality estimation for mobile H.264/AVC video streaming. J Commun 3(1):41–50
ITU-R Rec. BT. 500-10. Methodology for the subjective assessment of quality for television pictures (2012)
Furht B, Marqure O (eds) (2003) The handbook of video databases: design and applications. CRC Press, Boca Raton, pp 1041–1078
Yang C, Wang H, Po L (2007) Improved inter prediction based on structural similarity in H.264. In: IEEE International Conference on Signal Processing and Communications, vol 2, pp 340–343
Mai Z, Yang C, Kuang K, Po L (2006) A novel motion estimation method based on structural similarity for H.264 inter prediction. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol 2, pp 913–916
Ou T, Huang Y, Chen H (2010) A perceptual-based approach to bit allocation for H. 264 encoder. In: Proceeding of SPIE 7744, Visual Communications and Image Processing. https://doi.org/10.1117/12.863266
Wang S, Rehman A et al (2012) SSIM-motivated rate-distortion optimization for video coding. IEEE Trans Circuits Syst Video Technol 22(4):516–529
Ou T-S, Huang Y-H, Chen HH (2011) SSIM-based perceptual rate control for video coding. IEEE Trans Circuits Syst Video Technol 21(5):682–690
Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444
Han Y et al (2013) A new image fusion performance metric based on visual information fidelity. Inf Fusion 14(2):127–135
Geisler WS, Banks MS (1995) Visual performance. In: Bass M (ed) Handbook of optics. McGraw-Hill, New York
Cormack LK (2000) Computational models, of early human vision. In: Bovik A (ed) Handbook of image and video processing. Academic Press, London
Chuang H-M, Chen Y-S, Lin C-Y, Yu P-C (2016) Featuring the e-service quality of online website from a varied perspective. Hum Centric Comput Inf Sci 6:6. https://doi.org/10.1186/s13673-016-0058-1
Sarif BA, Pourazad MT, Nasiopoulos P et al (2015) Fairness scheme for energy efficient H.264/AVC-based video sensor network. Hum Centric Comput Inf Sci 5:7. https://doi.org/10.1186/s13673-015-0025-2
Lee SG, Cha EY (2016) Style classification and visualization of art painting’s genre using self-organizing maps. Hum Centric Comput Inf Sci 6:7. https://doi.org/10.1186/s13673-016-0063-4
Apple HLS. https://developer.apple.com/streaming
FFmpeg project. https://www.ffmpeg.org/
Acknowledgements
This work was supported by INHA UNIVERSITY Research Grant.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, H., Park, J. Efficient video quality assessment for on-demand video transcoding using intensity variation analysis. J Supercomput 75, 1751–1765 (2019). https://doi.org/10.1007/s11227-017-2215-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-017-2215-3