Compress compound images in H. 264/MPGE-4 AVC by exploiting spatial correlation
C. Lan, G. Shi, and F. Wu. IEEE Transactions on Image Processing, 19 (4):
946--957(2010)
Abstract
Compound images are a combination of text, graphics
and natural image. They present strong anisotropic features, especially
on the text and graphics parts. These anisotropic features
often render conventional compression inefficient. Thus, this
paper proposes a novel coding scheme from the H.264 intraframe
coding. In the scheme, two new intramodes are developed to better
exploit spatial correlation in compound images. The first is the
residual scalar quantization (RSQ) mode, where intrapredicted
residues are directly quantized and coded without transform. The
second is the base colors and index map (BCIM) mode that can be
viewed as an adaptive color quantization. In this mode, an image
block is represented by several representative colors, referred to
as base colors, and an index map to compress. Every block selects
its coding mode from two new modes and the previous intramodes
in H.264 by rate-distortion optimization (RDO). Experimental
results show that the proposed scheme improves the coding efficiency
even more than 10 dB at most bit rates for compound
images and keeps a comparable efficient performance to H.264 for
natural images.
%0 Journal Article
%1 lan2010compress
%A Lan, Cuiling
%A Shi, Guangming
%A Wu, Feng
%D 2010
%I IEEE
%J IEEE Transactions on Image Processing
%K Base_Colors_and_the_Index_Map Residual_Scalar_Quantization compound_image
%N 4
%P 946--957
%T Compress compound images in H. 264/MPGE-4 AVC by exploiting spatial correlation
%V 19
%X Compound images are a combination of text, graphics
and natural image. They present strong anisotropic features, especially
on the text and graphics parts. These anisotropic features
often render conventional compression inefficient. Thus, this
paper proposes a novel coding scheme from the H.264 intraframe
coding. In the scheme, two new intramodes are developed to better
exploit spatial correlation in compound images. The first is the
residual scalar quantization (RSQ) mode, where intrapredicted
residues are directly quantized and coded without transform. The
second is the base colors and index map (BCIM) mode that can be
viewed as an adaptive color quantization. In this mode, an image
block is represented by several representative colors, referred to
as base colors, and an index map to compress. Every block selects
its coding mode from two new modes and the previous intramodes
in H.264 by rate-distortion optimization (RDO). Experimental
results show that the proposed scheme improves the coding efficiency
even more than 10 dB at most bit rates for compound
images and keeps a comparable efficient performance to H.264 for
natural images.
@article{lan2010compress,
abstract = {Compound images are a combination of text, graphics
and natural image. They present strong anisotropic features, especially
on the text and graphics parts. These anisotropic features
often render conventional compression inefficient. Thus, this
paper proposes a novel coding scheme from the H.264 intraframe
coding. In the scheme, two new intramodes are developed to better
exploit spatial correlation in compound images. The first is the
residual scalar quantization (RSQ) mode, where intrapredicted
residues are directly quantized and coded without transform. The
second is the base colors and index map (BCIM) mode that can be
viewed as an adaptive color quantization. In this mode, an image
block is represented by several representative colors, referred to
as base colors, and an index map to compress. Every block selects
its coding mode from two new modes and the previous intramodes
in H.264 by rate-distortion optimization (RDO). Experimental
results show that the proposed scheme improves the coding efficiency
even more than 10 dB at most bit rates for compound
images and keeps a comparable efficient performance to H.264 for
natural images.},
added-at = {2011-08-17T11:21:17.000+0200},
author = {Lan, Cuiling and Shi, Guangming and Wu, Feng},
biburl = {https://www.bibsonomy.org/bibtex/216614662cfd7200e89fbd56d28cda3b4/armandinho},
interhash = {a1d5f9b499453ff5c0303ed19e8b7418},
intrahash = {16614662cfd7200e89fbd56d28cda3b4},
issn = {1057-7149},
journal = {IEEE Transactions on Image Processing},
keywords = {Base_Colors_and_the_Index_Map Residual_Scalar_Quantization compound_image},
number = 4,
pages = {946--957},
publisher = {IEEE},
school = {Department of Electrical Engineering, Xidian University, Microsoft Research Asia},
timestamp = {2011-08-17T11:21:17.000+0200},
title = {{Compress compound images in H. 264/MPGE-4 AVC by exploiting spatial correlation}},
volume = 19,
year = 2010
}