Color Image Compression System by using Block Categorization Based on Spatial Details and DCT Followed by Improved Entropy Encoder

Abstract

In this paper, a new high-performance lossy compression technique based on DCT is proposed. The image is partitioned into blocks of a size of NxN (where N is multiple of 2), each block is categorized whether it is high frequency (uncorrelated block) or low frequency (correlated block) according to its spatial details, this done by calculating the energy of block by taking the absolute sum of differential pulse code modulation (DPCM) differences between pixels to determine the level of correlation by using a specified threshold value. The image blocks will be scanned and converted into 1D vectors using horizontal scan order. Then, 1D-DCT is applied for each vector to produce transform coefficients. The transformed coefficients will be quantized with different quantization values according to the energy of the block. Finally, an enhanced entropy encoder technique is applied to store the quantized coefficients. To test the level of compression, the quantitative measures of the peak signal-to-noise ratio (PSNR) and compression ratio (CR) is used to ensure the effectiveness of the suggested system. The PSNR values of the reconstructed images are taken between the intermediate range from 28dB to 40dB, the best attained compression gain on standard Lena image has been increased to be around (96.60 %). Also, the results were compared to those of the standard JPEG system utilized in the “ACDSee Ultimate 2020” software to evaluate the performance of the proposed system.

High Synthetic Image Coding System

“…A set of results tables in our previous work [1] presents the system performance when applying the DCT module in terms of MSE, PSNR, SSIM, CR, CG, and BPP for Lena, Barbara, Peppers, and Baboon images, respectively. It shows that the fidelity level in terms of PSNR and SSIM increased while the CR and CG decreased.…”

“…Finally, the entropy encoder is applied to the quantized coefficients to store each coefficient with an optimal number of bits. The encoding and decoding process of DCT are described in detail in our original work in the following paper [1]. Figure 1 demonstrates the encoder and decoder units.…”

“…The next step is to decompose each vector by moving each nonzero value (> =1) into a new vector and replacing its original location with one. Then, the run length encoding process is applied to the original vector [1].…”

“…Finally, the adaptive shift coding technique is applied to assign the optimal number of bits required to store each coefficient [1].…”

Medical Image Compression and Encryption Using Adaptive Arithmetic Coding, Quantization Technique and RSA in DWT Domain

“…On the other side, the large size of medical images requires to be minimized before transmission or storage it. Data compression algorithms can use here to generate the output data with a smaller size [3]. Data compression techniques broadly are divided into two categories, lossless and lossy compression.…”