Subjective and Objective Quality Assessment of the SoftCast Video Transmission Scheme

• Main Authors: Trioux, Anthony, Valenzise, Giuseppe, Cagnazzo, Marco, Kieffer, Michel, Coudoux, Francois-Xavier, Corlay, Patrick, Gharbi, Mohamed
• Format: Conference Proceeding
• Language: English
• Subjects: Joint Source-Channel Coding; Linear Video Coding; Metadata; Observers; Quality assessment; Snow; SoftCast; Subjective Quality Assessment; Transforms; Transmitters; Visual Artifacts; Visualization
• ISSN: 2642-9357
• DOI: 10.1109/VCIP49819.2020.9301778

SoftCast-based linear video coding and transmission (LVCT) schemes have been proposed as a promising alternative to traditional video coding and transmission schemes in wireless environments. Currently, the performance of LVCT schemes is evaluated by means of traditional objective scores such as PSNR or SSIM. Nevertheless, since the compression is performed in a very different way from traditional coding schemes such as HEVC, visual artifacts are also quite different and deserve to be subjectively assessed. In this paper, we propose a subjective quality assessment of SoftCast, pioneer and standard of the LVCT schemes. This study aims to better understand the trade-offs between the LVCT parameters that can be tuned to improve the quality. These parameters, including different GoP-sizes, Compression Ratios (CR) and Channel Signal-to-Noise Ratio (CSNR), are used to generate a dataset of 85 videos. A Double Stimulus Impairment Scale (DSIS) test is performed on the received videos to assess the perceived quality. Results show that the key characteristic of SoftCast, the linear relation between CSNR and PSNR, is also observed with the Mean-Opinion Scores (MOS), except at high CSNR where the quality saturates. In addition, Bjøntegaard model is used to quantify the trade-offs between CR, GoP-size and CSNR, depending on the intended application. Finally, the performance of objective metrics compared to the obtained MOS is evaluated. Results show that Multi-Scale SSIM (MS-SSIM), SSIM and Video Multimethod Assessment Fusion (VMAF) metrics offer the best correlation with the MOS values.