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Publication - Professor David Bull

    Predicting video rate-distortion curves using textural features

    Citation

    Katsenou, A, Afonso, M, Agrafiotis, D & Bull, D, 2017, ‘Predicting video rate-distortion curves using textural features’. in: Picture Coding Symposium (PCS), 2016. Institute of Electrical and Electronics Engineers (IEEE)

    Abstract

    This work addresses the problem of predicting the compression efficiency of a video codec solely from features extracted from uncompressed content. Towards this goal, we have used a database of videos of homogeneous texture and extracted both spatial and frequency domain features. The videos are encoded using High Efficiency Video Coding (HEVC) reference codec at different quantization scales and their Rate-Distortion (RD) curves are modelled using linear regression. Using the extracted features and the fitted parameters of the RD model, a Support Vector Regression Model (SVRM) is trained to learn the relationship of the textural features with the RD curves. The SVRM is tested using iterative five-fold cross-validation. The presented experimental results demonstrate that RD curve characteristics can be predicted based on the textural features of the uncompressed videos, which offers potential benefits for encoder optimization.

    Full details in the University publications repository