Algorithms for Transform Selection in Multiple-Transform Video Compression

With a proper transform, an image or motion-compensated residual can be represented quite accurately with a small fraction of the transform coefficients. This is referred to as the energy compaction property. When multiple transforms are used, selecting the best transform for each block that leads to the best energy compaction is difficult. In this paper, we develop two algorithms to solve this problem. The first algorithm, which is computationally simple, leads to a locally optimal solution. The second algorithm, which is more computationally intensive, gives a globally optimal solution. We discuss the algorithms and their performance. Two-dimensional discrete cosine transform (2D-DCT) and direction-adaptive one-dimensional discrete cosine transforms (1D-DCTs) are used to evaluate the performance of our algorithms. Results obtained are consistent with their coding performance. As an application example of this paper, we apply our algorithm to evaluate the performance of a potential video compression system based on a very large number of transforms.

[1]  Manuel Blum,et al.  Time Bounds for Selection , 1973, J. Comput. Syst. Sci..

[2]  Jae S. Lim,et al.  Analysis of one-dimensional transforms in coding motion compensation prediction residuals for video applications , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  N. Ahmed,et al.  A derivation for the discrete cosine transform , 1982, Proceedings of the IEEE.

[4]  Thomas Wiegand,et al.  Draft ITU-T recommendation and final draft international standard of joint video specification , 2003 .

[5]  Antonio Ortega,et al.  Rate-distortion methods for image and video compression , 1998, IEEE Signal Process. Mag..

[6]  H. Kitajima Energy Packing Efficiency of the Hadamard Transform , 1976, IEEE Trans. Commun..

[7]  Kari Karhunen,et al.  Über lineare Methoden in der Wahrscheinlichkeitsrechnung , 1947 .

[8]  Min-Su Cheon,et al.  Improved Video Compression Efficiency Through Flexible Unit Representation and Corresponding Extension of Coding Tools , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Stéphane Mallat,et al.  Analysis of low bit rate image transform coding , 1998, IEEE Trans. Signal Process..

[10]  Fatih Kamisli Transforms for prediction residuals in video coding , 2010 .

[11]  Jae S. Lim,et al.  Algorithms for Transform Selection in Multiple-Transform Video Compression , 2013 .

[12]  Jianqin Zhou,et al.  On discrete cosine transform , 2011, ArXiv.

[13]  Marta Karczewicz,et al.  Improved h.264 intra coding based on bi-directional intra prediction, directional transform, and adaptive coefficient scanning , 2008, 2008 15th IEEE International Conference on Image Processing.

[14]  Jae S. Lim,et al.  Video compression with 1-D directional transforms in H.264/AVC , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Jorge Herbert de Lira,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[16]  Iain E. G. Richardson,et al.  H.264 and MPEG-4 Video Compression: Video Coding for Next-Generation Multimedia , 2003 .

[17]  Anastasis A. Sofokleous,et al.  Review: H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia , 2005, Comput. J..