Share Email Print
cover

Proceedings Paper

Compressed-domain video segmentation using wavelet transformation
Author(s): Hong Heather Yu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Video segmentation is an important first step towards automatic video indexing, retrieval, editing, and etc. However, the 'large' property of video makes it hard to handle in real time. To fulfill the goal of real-time processing, several factors need to be considered. First of all, indexing video directly in the compressed-domain offers the advantages of fast processing upon efficient storage. Secondly, extracting simple features with fast algorithms is no doubt helpful in speeding up the process. The questions are what kind of simple feature can characterize the changing statistics and what kind of algorithm can provide such feature with fast executability. In this paper, we propose a new automatic video segmentation scheme that utilizes wavelet transformation based on the following consideration: wavelet is a nice tool for subband decomposition, it encodes both frequency and spatial information; more over, it is easy to program and fast to execute. In the last decade or so, wavelet transform is emerged to image/video signal processing for analyzing functions at different levels of details. In particular, wavelet, as a tool, has been widely used in the area of image compression. In image compression, it is possible to recover a fairly accurate representation of the image by saving the few largest wavelet coefficients (and throwing away part or all of the smaller coefficients). By using this property, we extract a discrimination signature of each image from a few large coefficients for each color channel. The system works on the compressed video that does not require full decoding of the video and performs a wavelet transformation on the extracted video data. The signature (as feature) is extracted from the wavelet coefficients to characterize the changing statistics of shot transitions. Cuts, fades, and dissolve are detected based on the analysis of the changing statistics curve.

Paper Details

Date Published: 18 October 1999
PDF: 8 pages
Proc. SPIE 3808, Applications of Digital Image Processing XXII, (18 October 1999); doi: 10.1117/12.365830
Show Author Affiliations
Hong Heather Yu, Panasonic Information and Networking Technology Lab. (United States)


Published in SPIE Proceedings Vol. 3808:
Applications of Digital Image Processing XXII
Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top