Mapping of Temporal Space Slicing for Video Quality Metrics Assessment

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 1

Year of Publication : 2024

Authors : Shilpa Bagade, Budati Anil Kumar, L. Koteswara Rao

10.14445/23488379/IJEEE-V11I1P101

How to Cite?

Shilpa Bagade, Budati Anil Kumar, L. Koteswara Rao, "Mapping of Temporal Space Slicing for Video Quality Metrics Assessment," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 1, pp. 1-7, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I1P101

Abstract:

Full-Reference (FR) video quality evaluation approach that combines frame-based Visual Quality Assessment (VQA) with analysis of space-time slices to provide an efficient video quality predictor is proposed. The sample and test video clips are first put into a temporal space slice form by the proposed method. Each reference-test video pair is subjected to the computation of several distortion-aware maps to define space-time distortions more thoroughly. Then, a standard visual quality model, such as Peak Signal to Noise Ratio (PSNR) or Structural Similarity Index (SSIM), is used to process these reference-distorted maps. A final video quality score is created by combining several VQA outputs using a straightforward, learnt pooling method. The method thoroughly evaluated the Temporal Space Slicing (TSS) algorithm using three publicly accessible video quality assessments and discovered that TSS-PSNR performed noticeably better than leading-edge video quality models.

Keywords:

HEVC, Packet loss, Video streaming, Video compression, Video Quality Metrics.

References:

[1] Stefan Winkler, and Praveen Mohandas, “The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics,” IEEE Transactions on Broadcasting, vol. 54, no. 3, pp. 660-668, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Zhengzhong Tu et al., “UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content,” IEEE Transactions on Image Processing, vol. 30, pp. 4449-4464, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Xiaochen Liu et al., “Speeding Up Subjective Video Quality Assessment via Hybrid Active Learning,” IEEE Transactions on Broadcasting, vol. 69, no. 1, pp. 165-178, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] K.T. Tan, and M. Ghanbari, “A Multi-Metric Objective Picture-Quality Measurement Model for MPEG Video,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 10, no. 7, pp. 1208-1213, 2000.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Miltiadis Alexios Papadopoulos et al., “A Multi-Metric Approach for Block-Level Video Quality Assessment,” Signal Processing: Image Communication, vol. 78, pp. 152-158, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Fan Zhang, and David R. Bull, “A Perception-Based Hybrid Model for Video Quality Assessment,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 6, pp. 1017-1028, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Gary J. Sullivan et al., “Overview of the High Efficiency Video Coding (HEVC) Standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1649-1668, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[8] T. Wiegand et al., “Overview of the H.264/AVC Video Coding Standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 7, pp. 560-576, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Frank Bossen et al., “HEVC Complexity and Implementation Analysis,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1685-1696, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Jens-Rainer Ohm et al., “Comparison of the Coding Efficiency of Video Coding Standards-Including High Efficiency Video Coding (HEVC),” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp. 1669-1684, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jian Qiao, Yejun He, and Xuemin Sherman Shen, “Improving Video Streaming Quality in 5G Enabled Vehicular Networks,” IEEE Wireless Communications, vol. 25, no. 2, pp. 133-139, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Xin Feng et al., “Saliency Inspired Full-Reference Quality Metrics for Packet-Loss-Impaired Video,” IEEE Transactions on Broadcasting, vol. 57, no. 1, pp. 81-88, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Kalpana Seshadrinathan et al., “Study of Subjective and Objective Quality Assessment of Video,” IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1427-1441, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Pradip Paudyal, Federica Battisti, and Marco Carli, “Impact of Video Content and Transmission Impairments on Quality of Experience,” Multimedia Tools and Applications, vol. 75, pp. 16461-16485, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Yi J. Liang, John G. Apostolopoulos, and Bernd Girod, “Analysis of Packet Loss for Compressed Video: Effect of Burst Losses and Correlation between Error Frames,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 7, pp. 861-874, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Songnan Li et al., “Image Quality Assessment by Separately Evaluating Detail Losses and Additive Impairments,” IEEE Transactions on Multimedia, vol. 13, no. 5, pp. 935-949, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Monalisa Ghosh, and Chetna Singhal, “MO-QoE: Video QoE Using Multi-Feature Fusion Based Optimized Learning Models,” Signal Processing: Image Communication, vol. 107, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] H. Hamdoun et al., “Performance Benefits of Network Coding for HEVC Video Communications in Satellite Networks,” Iranian Journal of Electrical and Electronic Engineering, vol. 17, no. 3, pp. 1–10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Glenn Van Wallendael et al., “Keyframe Insertion: Enabling Low-Latency Random Access and Packet Loss Repair,” Electronics, vol. 10, no. 6, pp. 1-17, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Mohammad Kazemi, Mohammad Ghanbari, and Shervin Shirmohammadi, “The Performance of Quality Metrics in Assessing Error-Concealed Video Quality,” IEEE Transactions on Image Processing, vol. 29, pp. 5937-5952, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Lixiong Liu et al., “Video Quality Assessment Using Space-Time Slice Mappings,” Signal Processing: Image Communication, vol. 82, 2020.[CrossRef] [Google Scholar] [Publisher Link]