Author:
Gaoyong Luo
Affiliation:
Faculty of Technology, Buckinghamshire Chilterns University College, United Kingdom
Keyword(s):
Image embedded coding, Edge preservation, Local variance analysis, Bit rate allocation, Error resilience.
Related
Ontology
Subjects/Areas/Topics:
Image and Video Processing, Compression and Segmentation
;
Multimedia
;
Multimedia Signal Processing
;
Telecommunications
Abstract:
Embedded coding for progressive image transmission has recently gained popularity in image compression community. However, current progressive wavelet-based image coders tend to be complex and computationally intense requiring large memory space. The encoding process usually sends information on the lowest-frequency wavelet coefficients first. At very low bit rates, images compressed are therefore dominated by low frequency information, where high frequency components belonging to edges are lost leading to blurring the signal features. This paper presents a new image coder for real-time transmission, employing edge preservation based on local variance analysis to improve the visual appearance and recognizability of compressed images. The analysis and compression is performed by dividing an image into blocks. Lifting wavelet filter bank is constructed for image decomposition and reconstruction with the advantages of being computationally efficient and boundary effects minimized. A mod
ified SPIHT algorithm with more bits used to encode the wavelet coefficients and transmitting fewer bits in the sorting pass for performance improvement, is used to reduce the correlation of the coefficients at scalable bit rates. Local variance estimation and edge strength measurement can effectively determine the best bit allocation for each block to preserve the local features. Experimental results demonstrate that the method performs well both visually and in terms of quantitative performance measures, and offers error resilience feature that is evaluated using a simulated transmission channel with random error.
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