No-reference quality assessment of H.264/AVC encoded video based on natural scene features

Kongfeng Zhu; Vijayan Asari; Dietmar Saupe

doi:10.1117/12.2015594

28 May 2013 No-reference quality assessment of H.264/AVC encoded video based on natural scene features

Kongfeng Zhu, Vijayan Asari, Dietmar Saupe

Author Affiliations +

Proceedings Volume 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013; 875505 (2013) https://doi.org/10.1117/12.2015594
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

H.264/AVC coded video quality is crucial for evaluating the performance of consumer-level video camcorders and mobile phones. In this paper, a DCT-based video quality prediction model (DVQPM) is proposed to blindly predict the quality of compressed natural videos. The model is frame-based and composed of three steps. First, each decoded frame of the video sequence is decomposed into six feature maps based on the DCT coefficients. Then five efficient frame-level features (kurtosis, smoothness, sharpness, mean Jensen Shannon divergence, and blockiness) are extracted to quantify the distortion of natural scenes due to lossy compression. In the last step, each frame-level feature is averaged across all frames (temporal pooling); a trained multilayer neural network takes the five features as inputs and outputs a single number as the predicted video quality. The DVQPM model was trained and tested on the H.264 videos in the LIVE Video Database. Results show that the objective assessment of the proposed model has a strong correlation with the subjective assessment.

Citation Download Citation

Kongfeng Zhu, Vijayan Asari, and Dietmar Saupe "No-reference quality assessment of H.264/AVC encoded video based on natural scene features", Proc. SPIE 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013, 875505 (28 May 2013); https://doi.org/10.1117/12.2015594

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