Decision trees for denoising in H.264/AVC video sequences

G. Huchet; J.-Y. Chouinard; D. Wang; A. Vincent

doi:10.1117/12.766406

28 January 2008 Decision trees for denoising in H.264/AVC video sequences

G. Huchet, J.-Y. Chouinard, D. Wang, A. Vincent

Proceedings Volume 6822, Visual Communications and Image Processing 2008; 68220Y (2008) https://doi.org/10.1117/12.766406
Event: Electronic Imaging, 2008, San Jose, California, United States

Abstract

All existing video coding standards are based on block-wise motion compensation and block-wise DCT. At high levels of quantization, block-wise motion compensation and transform produces blocking artifacts in the decoded video, a form of distortion to which the human visual system is very sensitive. The latest video coding standard, H.264/AVC, introduces a deblocking filter to reduce the blocking artifacts. However, there is still visible distortion after the filtering when compared to the original video. In this paper, we propose a non-conventional filter to further reduce the distortion and to improve the decoded picture quality. Different from conventional filters, the proposed filter is based on a machine learning algorithm (decision tree). The decision trees are used to classify the filter's inputs and select the best filter coeffcients for the inputs. Experimental results with 4 × 4 DCT indicate that using the filter holds promise in improving the quality of H.264/AVC video sequences.

Citation Download Citation

G. Huchet, J.-Y. Chouinard, D. Wang, and A. Vincent "Decision trees for denoising in H.264/AVC video sequences", Proc. SPIE 6822, Visual Communications and Image Processing 2008, 68220Y (28 January 2008); https://doi.org/10.1117/12.766406

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available