A Smoothness Constraint Set based on Local Statistics of BDCT Coefficients for Image Postprocessing
Author(s)
Gan, XC
Liew, AWC
Yan, H
Griffith University Author(s)
Year published
2005
Metadata
Show full item recordAbstract
In blocking artifacts reduction based on the projection onto convex sets (POCS) technique, good constraint sets are very important. Until recently, smoothness constraint sets (SCS) are often formulated in the image domain, whereas quantization constraint set is defined in the block-based discrete cosine transform (BDCT) domain. Thus, frequent BDCT transform is inevitable in alternative projections. In this paper, based on signal and quantization noise statistics, we proposed a novel smoothness constraint set in the BDCT transform domain via the Wiener filtering concept. Experiments show that POCS using this smoothness ...
View more >In blocking artifacts reduction based on the projection onto convex sets (POCS) technique, good constraint sets are very important. Until recently, smoothness constraint sets (SCS) are often formulated in the image domain, whereas quantization constraint set is defined in the block-based discrete cosine transform (BDCT) domain. Thus, frequent BDCT transform is inevitable in alternative projections. In this paper, based on signal and quantization noise statistics, we proposed a novel smoothness constraint set in the BDCT transform domain via the Wiener filtering concept. Experiments show that POCS using this smoothness constraint set not only has good convergence but also has better objective and subjective performance. Moreover, this set can be used as extra constraint set to improve most existing POCS-based image postprocessing methods.
View less >
View more >In blocking artifacts reduction based on the projection onto convex sets (POCS) technique, good constraint sets are very important. Until recently, smoothness constraint sets (SCS) are often formulated in the image domain, whereas quantization constraint set is defined in the block-based discrete cosine transform (BDCT) domain. Thus, frequent BDCT transform is inevitable in alternative projections. In this paper, based on signal and quantization noise statistics, we proposed a novel smoothness constraint set in the BDCT transform domain via the Wiener filtering concept. Experiments show that POCS using this smoothness constraint set not only has good convergence but also has better objective and subjective performance. Moreover, this set can be used as extra constraint set to improve most existing POCS-based image postprocessing methods.
View less >
Journal Title
Image and Vision Computing
Volume
23
Issue
8
Publisher URI
Subject
Artificial Intelligence and Image Processing
Electrical and Electronic Engineering