Low complex blind video quality predictor based on support vector machines

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Authors

Amitesh Kumar Singam¹ , Venkat Raj Reddy Pashike¹

Conference / event: IEEE Sweden GOLD AG, IEEE STEP event, June 2012 (Karlskrona, Sweden)

Poster summary: Objective Video Quality Assessment plays an important role in visual processing systems and specially in mobile communication field, some of video applications boosted the interest in need of robust methods for No-Reference(NR) Objective Video Quality Assessment where the handiness of reference video is not available. Our challenge lies in formulating and melding effective features into one model based on human visualizing characteristics. Our research work explores the tradeoffs between quality prediction and complexity of system. We implemented support vector regression algorithm as NR-based Video Quality Metric(VQM) for quality estimation with simplified input features. The features are obtained from extraction of H.264 Bit stream data at decoder side of network. Our metric has predicted with good correlation for all deployed metrics and the obtained results demonstrates robustness of our approach.

Research areas: Electrical Engineering, Education, Computer and Information Science , Mathematics

References

Amitesh Kumar singam, Venkat Pashike. Low Complex Blind Video Quality Predictor based on Support Vector Machines. Computer Science [cs]. Telecommunications, 2018. English. ⟨NNT : ⟩. ⟨tel-04036816⟩

Additional information

Data availability statement: The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

Creative Commons license: Copyright © 2023 Singam et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.