IEEE 2014 JAVA IMAGE PROCESSING PROJECTS Hierarchical prediction and context adaptive coding for lossless color image compression

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    14-Jun-2015

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1. GLOBALSOFT TECHNOLOGIESIEEE PROJECTS & SOFTWARE DEVELOPMENTSIEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEEBULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA…

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1. GLOBALSOFT TECHNOLOGIESIEEE PROJECTS & SOFTWARE DEVELOPMENTSIEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEEBULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTSCELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.comHierarchical Prediction and Context Adaptive Coding forLossless Color Image CompressionAbstractThis paper presents a new lossless color image compression algorithm, based on thehierarchical prediction and context-adaptive arithmetic coding. For the lossless compressionof an RGB image, it is first decorrelated by a reversible color transform and then Ycomponent is encoded by a conventional lossless grayscale image compression method.For encoding the chrominance images, we develop a hierarchical scheme that enables theuse of upper, left, and lower pixels for the pixel prediction, whereas the conventional rasterscan prediction methods use upper and left pixels. An appropriate context model for theprediction error is also defined and the arithmetic coding is applied to the error signalcorresponding to each context. For several sets of images, it is shown that the proposedmethod further reduces the bit rates compared with JPEG2000 and JPEG-XR.Existing systemThis paper presents a new lossless color image compression algorithm, based on thehierarchical prediction and context-adaptive arithmetic coding. For the lossless compressionof an RGB image, it is first decorrelated by a reversible color transform and then Ycomponent is encoded by a conventional lossless grayscale image compression method.Proposed systemFor encoding the chrominance images, we develop a hierarchical scheme that enables theuse of upper, left, and lower pixels for the pixel prediction, whereas the conventional rasterscan prediction methods use upper and left pixels. An appropriate context model for theprediction error is also defined and the arithmetic coding is applied to the error signalcorresponding to each context. For several sets of images, it is shown that the proposedmethod 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 GBSOFTWARE CONFIGURATION:- Operating System : Windows XP Programming Language : JAVA Java Version : JDK 1.6 & above.

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