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Publication - Professor David Bull

    Video texture analysis based on HEVC encoding statistics


    Afonso, M, Katsenou, A, Zhang, A, Agrafiotis, D & Bull, D, 2017, ‘Video texture analysis based on HEVC encoding statistics’. in: Picture Coding Symposium (PCS), 2016. Institute of Electrical and Electronics Engineers (IEEE)


    In this paper, an extensive study of different video texture properties based on encoding statistics extracted from the HEVC HM reference software is presented. Mode selection, partitioning, motion vectors and bitrate allocation are among the statistics obtained from the encoder. For this study, a new dataset
    of homogeneous static and dynamic video textures, HomTex, is proposed. A comprehensive investigation of the results reveals a significant variability of coding statistics within dynamic textures, suggesting that this category should be further split into two relevant subcategories, continuous dynamic textures and discrete dynamic textures. This case is supported by an unsupervised
    learning approach on the statistics extracted. Finally, following the results obtained, some suggestions of improvements in video texture coding are presented.

    Full details in the University publications repository