Transform coding of monochrome images with a statistical design of experiments approach to separate noise

Authors:
R. Krishnamoorthi
Affiliations:
Department of Information Technology, Bharathidasan Institute of Technology, Bharathidasan University, Trichy 620 024, Tamilnadu, India
Venue:
Pattern Recognition Letters
Year:
2007

Citing 6
Cited 5

An orthogonal polynomials based framework for edge detection in 2-D monochrome images

Pattern Recognition Letters
Fast multiplierless approximations of the DCT with the liftingscheme

IEEE Transactions on Signal Processing
The JPEG still picture compression standard

IEEE Transactions on Consumer Electronics
Applications of universal context modeling to lossless compression of gray-scale images

IEEE Transactions on Image Processing
A new approach for subset 2-D AR model identification for describing textures

IEEE Transactions on Image Processing
Recursive estimation of images using non-Gaussian autoregressive models

IEEE Transactions on Image Processing

A new integer image coding technique based on orthogonal polynomials

Image and Vision Computing
Minimum distortion clustering technique for orthogonal polynomials transform vector quantizer

Proceedings of the 2011 International Conference on Communication, Computing & Security
A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model

Journal of Visual Communication and Image Representation
Indexing and retrieval of visually similar images in the orthogonal polynomials transform domain

ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
An edge preserving requantization model for color image coding with orthogonal polynomials

Digital Signal Processing

Quantified Score

Hi-index	0.11

Visualization

Abstract

A new transform coding technique based on a set of orthogonal polynomials has been proposed in this paper. In the transformed domain, statistical design of experiments approach is used to separate the spatial variation due to discriminable low level features (signals) from the spatial variation due to an unexplained source called noise. The proposed polynomial transform has a low computational complexity because it is configured as an integer transform. The proposed transform also has very good compression ability or separationability. The degree of this separationability can be controlled by specifying the level of the signal-to-noise ratio.