Learning dictionaries for matching pursuits based video coders

Author

Schmid-Saugeon, Philippe ; Zakhor, Avideh

Volume

3

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

562

Abstract

We present a learning scheme for designing dictionaries of two-dimensional functions for matching pursuits (MP) based video coding. The motivation is to improve the performance of such codecs by adapting the structure of the dictionary functions to specific bit-rates or types of sequences. The scheme we propose is based on vector quantization (VQ), and uses an inner-product based distortion measure. The different processing steps, consisting of data extraction from the motion compensated error frames, training, pruning, and testing, are presented in detail. We find that for high bit-rate QCIF sequences we can achieve improvements of up to 0.66 dB

Keywords

image sequences; learning (artificial intelligence); motion compensation; vector quantisation; video codecs; video coding; QCIF sequences; VQ; data extraction; distortion measure; error frames; learning dictionaries; matching pursuits; motion compensation; two-dimensional functions; vector quantization; video codecs; video coding; Data mining; Dictionaries; Discrete cosine transforms; Distortion measurement; Iterative algorithms; Matching pursuit algorithms; Testing; Vector quantization; Video codecs; Video coding;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2001. Proceedings. 2001 International Conference on

Conference_Location

Thessaloniki

Print_ISBN

0-7803-6725-1

Type

conf

DOI

10.1109/ICIP.2001.958176

Filename

958176