article · 2011
Reducedcomplexity entropy coding of transform coefficient levels using truncated Golomb-Rice codes in video compression
Detlev Marpe, Heiko Schwarz, Thomas Wieg
Cited by 0Abstract and details only
Abstract
In hybrid video coding, the difference between the intra or inter prediction signal and the original signal is transmitted using block-based transform coding. The state-of-the-art in coding the quantized transform coefficients is the approach specified in H.264/AVC for context-adaptive binary arithmetic coding. It has, however, been shown that the number of binary symbols that have to be arithmetically coded for the transform coefficients can become very large, making the concept less attractive for high rate applications. To overcome this issue, we propose a combination of simple variable-length codes and context-adaptive binary coding, which yields the same coding efficiency as the H.264/AVC transform coefficient coding at a lower complexity level and which has been adopted into the HEVC test model (HM). Index Terms — Transform coding, video compression 1.
Cite this paper
Marpe, D., Schwarz, H., & Wieg, T. (2011). Reducedcomplexity entropy coding of transform coefficient levels using truncated Golomb-Rice codes in video compression.
Marpe, Detlev, et al. “Reducedcomplexity entropy coding of transform coefficient levels using truncated Golomb-Rice codes in video compression.” 2011.
Marpe, Detlev, Heiko Schwarz, and Thomas Wieg. 2011. “Reducedcomplexity entropy coding of transform coefficient levels using truncated Golomb-Rice codes in video compression.”
Marpe, D., Schwarz, H. and Wieg, T. (2011) ‘Reducedcomplexity entropy coding of transform coefficient levels using truncated Golomb-Rice codes in video compression’.
@article{marpe2011reducedcomplexity,
title = {{Reducedcomplexity entropy coding of transform coefficient levels using truncated Golomb-Rice codes in video compression}},
author = {Marpe, Detlev and Schwarz, Heiko and Wieg, Thomas},
year = {2011}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.