Paper Info
Title
A New Shape-Vector Quantization-Based Adaptive Predictive Image Coder
Abstract
In this paper, a new lossless image compression technique, shape-vector quantization (VQ)-based adaptive predictive coder (SAPC), is introduced. In the proposed scheme, the local shape information of the image block is obtained through shape-VQ, This information is utilized by a novel predictive coder, shape-differential pulse code modulation (DPCM), to adaptively select the optimum predictor on a pixel-by-pixel basis. The prediction errors can be further compressed by an error-adjusting process. The proposed scheme achieves a breakthrough in prediction by utilizing the local feature of the image block through shape-VQ, thus improving the accuracy of the prediction while reducing the overhead of the side information. It also simplifies the complicated procedures involved in the computation of the prediction parameters. Although the proposed scheme outperforms many traditional lossless image-coding schemes, it produces comparable results to the newly developed context-based scheme with lower computational complexity. On the basis of the promising compression results, the proposed scheme could be the best candidate for the lossless image coding. (C) 2000 John Wiley & Sons, Inc.
Year
DOI
Venue
1999
10.1002/(SICI)1098-1098(1999)10:6<419::AID-IMA2>3.0.CO;2-5
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Field
DocType
Volume
Computer vision,Dictionary coder,Computer science,Speech recognition,Vector quantization,Artificial intelligence
Journal
10
Issue
ISSN
Citations 
6
0899-9457
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Jian Wang110.73
Golshah Naghdy2299.36