Image Compression Using Row & Column Based Cosine Error Vector Rotation (TCEVR) and Error Vector Rotation Using Walsh Codebook Generation In Different Quantization Matrix

Abstract

Image compression is the application of Data compression on digital images. The objective of image compression is to reduce redundancy of the image data in order to be able to store or transmit data in an efficient form. Image compression can be lossy or lossless. Lossless compression is sometimes preferred for artificial images such as technical drawings, icons or comics. This is because lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossless compression methods may also be preferred for high value content, such as medical imagery or image scans made for archival purposes. Lossy methods are especially suitable for natural images such as photos in applications where minor loss of fidelity is acceptable to achieve a substantial reduction in bit rate. Image compression schemes are generally classified as lossless compression schemes and lossy compression schemes. Lossless compression involves compressing data which, when decompressed, will be an exact replica of the original data. To Apply row compression technique to compress the image apply column Compression to compress the image . To Apply the Quantization Matrix to compress the image. The Different parameters such as Time taken from compression, Compression ratio, time taken for decompression and Peak Signal to noise ratio are calculated and maximum PSNR is 35.8 calculated in this work.