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

Kongfeng Zhu | Zendy; Vijayan K. Asari | Zendy; Dietmar Saupe | Zendy

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

Author(s) -

Kongfeng Zhu,

Vijayan K. Asari,

Dietmar Saupe

Publication year - 2013

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.2015594

Subject(s) - computer science , video quality , artificial intelligence , computer vision , data compression , discrete cosine transform , video compression picture types , lossy compression , feature (linguistics) , inter frame , pyramid (geometry) , frame (networking) , block matching algorithm , multiview video coding , scalable video coding , pooling , rate–distortion optimization , reference frame , pattern recognition (psychology) , motion compensation , video tracking , video processing , mathematics , image (mathematics) , telecommunications , metric (unit) , linguistics , operations management , philosophy , geometry , economics

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.

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