Parametric Video Compression

This project presents a novel means of video compression based on texture warping and synthesis. Instead of encoding whole images or prediction residuals after translational motion estimation, our algorithm employs a perspective motion model to warp static textures and utilises texture synthesis to create dynamic textures. Texture regions are segmented using features derived from the complex wavelet transform and further classified according to their spatial and temporal characteristics. Moreover, a compatible artefact-based video metric (AVM) is proposed with which to evaluate the quality of the reconstructed video. This is also employed in-loop to prevent warping and synthesis artefacts. The proposed algorithm has been integrated into an H.264 video coding framework. The results show significant bitrate savings, of up to 60% compared with H.264 at the same objective quality (based on AVM) and subjective scores.

It is currently a very exciting and challenging time for video compression. The predicted growth in demand for bandwidth, especially for mobile services will be driven by video applications and is probably greater now than it has ever been. David Bull (VI-Lab), Dimitris Agrafiotis (VI-Lab) and Roland Baddeley (Experimental Psychology) have won a new £600k EPSRC research grant to investigate perceptual redundancy in, and new representations for digital video content. With EPSRC funding and collaboration with BBC and HHIFraunhofer Berlin, the team will investigate video compression schemes where an analysis/synthesis framework replaces the conventional energy minimisation approach. A preliminary coding framework of this type has been created by Zhang and Bull where scene content is modelled, using computer graphic techniques to replace target textures at the decoder. This approach is already producing world-leading results and has the potential to create a new content-driven framework for video compression, where region-based parameters are combined with perceptual quality metrics to inform and drive the coding processes.

Published Work

  1. P. Ndjiki-Nya, D. Doshkova, H. Kaprykowsky, F. Zhang, D. Bull, T. Wiegand, Perception-oriented video coding based on image analysis and completion: A review, Signal Processing: Image Communication, Volume 27, Issue 6, July 2012, Pages 579–594Link
  2. Zhang, F and Bull D.R., A Parametric Framework For Video Compression Using Region-based Texture Models’, IEEE Journal on Selected Areas in Signal processing (Special Issue), Vol. 5, No. 7, November 2011, pp1378-92. Link
  3. Ierodiaconou, S.; Byrne, J.; Bull, D.R.; Redmill, D.; Hill, P.; Unsupervised image compression using graphcut texture synthesis, Image Processing (ICIP), 2009 16th IEEE International Conference on, 2009, Page(s): 2289 – 2292
  4. Byrne, J.; Ierodiaconou, S.; Bull, D.; Redmill, D.; Hill, P.; Unsupervised image compression-by-synthesis within a JPEG framework, Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on, 2008 , Page(s): 2892 – 2895

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