Lightweight implementation of No-Reference (NR) perceptual quality assessment of H.264/AVC compression

• We propose NR metric implementation to objectively assess H.264/AVC quality.
• Proposed model takes into account typical artefacts introduced by video codecs.
• Accuracy of proposed quality metric is validated over subjective data and SSIM.
• Our metric has higher Pearson׳s correlation with subjective scores than SSIM.
• Our implementation is lightweight to perform real-time quality calculations.

The paper proposes a No-Reference (NR) metric to objectively assess H.264/AVC video quality and its lightweight implementation. The proposed model takes into account typical artefacts introduced by hybrid block-based motion-compensated predictive video codecs such as the one related to the H.264/AVC standard. More specifically, these artefacts are the blockiness introduced at the boundaries of each coded block, and the temporal flickering due to different coding modes used for the same macro-block along the video sequence. Furthermore, a flickering metric for intra-coded frames is also derived. The quality prediction accuracy of the proposed NR quality metric is validated over subjective data collected during a subjective video evaluation experiment. Moreover, quality prediction accuracy is also compared with that provided by the well-known state-of-the-art Structural Similarity Index Metric (SSIM) which works in a Full-Reference (FR) mode. The proposed metric achieves a higher Pearson׳s correlation coefficient with subjective scores than the one achieved by the SSIM metric. The proposed implementation of algorithms is sufficiently lightweight to perform real-time calculations of video streaming quality on PCs in order to assure a Quality of Experience (QoE)-aware service.

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