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Predicting video rate-distortion curves using textural features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationPicture Coding Symposium (PCS), 2016
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781509059669
ISBN (Print)9781509059676
StatePublished - 24 Apr 2017


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.

    Research areas

  • Feature extraction, Dynamics, Correlation, Manganese, Bit rate, Enthropy, Video sequences

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 712 KB, PDF-document


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