Abstract:
In most digital cameras, Bayer color filter array (CFA) images are captured and demosaicing is generally carried out before compression. Recently, it was found that compression- first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression scheme for Bayer CFA images is proposed in this paper. It exploits a context matching technique to rank the neighboring pixels when predicting a pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy when handling red and blue samples, and an adaptive codeword generation technique to adjust the divisor of Rice code for encoding the prediction residues. Simulation results show that the proposed compression scheme can achieve a better compression performance than conventional lossless CFA image coding schemes.
Existing System:-
• Difficult to identify the neighboring pixels for predicting the existing samples value of the pixel.
• No encoding techniques are used to encode the samples of the images.
Proposed System:-
• Context matching technique is used to rank the neighbor pixels for predicting the existing samples value of the pixel.
• Rice code encoding technique is used to encode the samples of the images.
• This will provide best compression ratio.
Hardware Interface:-
[
• Hard disk : 40 GB
• RAM : 512 MB
• Processor Speed : 3.00GHz
• Processor : Pentium IV Processor
Software Interface:-
• JDK 1.5
• Swing Builder
• MS-SQL Server
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