Abstract
The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio with high image quality.
Highlights
Data compression is utilized to limit the amount of data used to format an image, video, sound, or file content without intense reduction in the resolution of the original data
The system was examined on various medical images and the results demonstrated significant improvement in overall performance parameters, such as peak signal-to-noise ratio (PSNR) and Compression Ratio (CR)
Step2: Converting the color space of the input image to YCbCr color model according to equation (1) [11]
Summary
Data compression is utilized to limit the amount of data used to format an image, video, sound, or file content without intense reduction in the resolution of the original data. Image compression is the application of data compression on digital images to lessen the amount of data required to represent a digital image, by lowering the redundancy and irrelevancy of data that format the image, to be able to transmit or save the image in an efficient representation [1, 2]. Image compressions may be either lossless or lossy. The original image is identical to the reconstructed one. No information is lost during the compression and decompression stages. Lossless compression is selected to be used for certain critical applications in fields such as
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