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- W3211421748 abstract "Encoding textural content remains a challenge for current standardised video codecs. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimise encoding performance. In this paper, we analyse the spatio-temporal features and statistics of video textures, explore the rate-quality performance of different texture types and investigate models to mathematically describe them. For all considered theoretical models, we employ machine-learning regression to predict the rate-quality curves based solely on selected spatio-temporal features extracted from uncompressed content. All experiments were performed on homogeneous video textures to ensure validity of the observations. The results of the regression indicate that using an exponential model we can more accurately predict the expected rate-quality curve (with a mean Bjøntegaard Delta rate of .46% over the considered dataset), while maintaining a low relative complexity. This is expected to be adopted by in the loop processes for faster encoding decisions such as rate–distortion optimisation, adaptive quantisation, partitioning, etc. • Study of different video texture types and their relation to compression performance. • Video texture rate–distortion curve prediction based on low-level features extracted from uncompressed content. • Comparative analysis of different models on their prediction performance and their computational complexity." @default.
- W3211421748 created "2021-11-22" @default.
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- W3211421748 date "2022-02-01" @default.
- W3211421748 modified "2023-10-03" @default.
- W3211421748 title "Study of compression statistics and prediction of rate-distortion curves for video texture" @default.
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- W3211421748 doi "https://doi.org/10.1016/j.image.2021.116551" @default.
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