This document describes a new lossless color image compression algorithm based on hierarchical prediction and context-adaptive arithmetic coding. It decorrelates RGB images using a reversible color transform, then encodes the Y component conventionally and the chrominance components hierarchically using upper, left, and lower pixels for prediction rather than just upper and left. An appropriate context model is defined for prediction errors, which are arithmetic coded. Testing showed this method achieves better bit rate compression than JPEG2000 and JPEG-XR.

1. GLOBALSOFT TECHNOLOGIES
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Hierarchical Prediction and Context Adaptive Coding for
Lossless Color Image Compression
Abstract
This paper presents a new lossless color image compression algorithm, based on the
hierarchical prediction and context-adaptive arithmetic coding. For the lossless compression
of an RGB image, it is first decorrelated by a reversible color transform and then Y
component is encoded by a conventional lossless grayscale image compression method.
For encoding the chrominance images, we develop a hierarchical scheme that enables the
use of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster
scan prediction methods use upper and left pixels. An appropriate context model for the
prediction error is also defined and the arithmetic coding is applied to the error signal
corresponding to each context. For several sets of images, it is shown that the proposed
method further reduces the bit rates compared with JPEG2000 and JPEG-XR.
Existing system
This paper presents a new lossless color image compression algorithm, based on the
hierarchical prediction and context-adaptive arithmetic coding. For the lossless compression
of an RGB image, it is first decorrelated by a reversible color transform and then Y
component is encoded by a conventional lossless grayscale image compression method.
Proposed system
For encoding the chrominance images, we develop a hierarchical scheme that enables the
use of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster
scan prediction methods use upper and left pixels. An appropriate context model for the
prediction error is also defined and the arithmetic coding is applied to the error signal
corresponding to each context. For several sets of images, it is shown that the proposed
method further reduces the bit rates compared with JPEG2000 and JPEG-XR.

2. SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.