Hybrid DWT-DCT compression algorithm & a new flipping block with an adaptive RLE method for high medical image compression ratio

Abstract

Huge number of medical images are generated and needs for more storage capacity and bandwidth for transferring over the networks. Hybrid DWT-DCT compression algorithm is applied to compress the medical images by exploiting the features of both techniques. Discrete Wavelet Transform (DWT) coding is applied to image YCbCr color model which decompose image bands into four subbands (LL, HL, LH and HH). The LL subband is transformed into low and high frequency components using Discrete Cosine Transform (DCT) to be quantize by scalar quantization that was applied on all image bands, the quantization parameters where reduced by half for the luminance band while it is the same for the chrominance bands to preserve the image quality, the zigzag scan is applied on the quantized coefficients and the output are encoded using DPCM, shift optimizer and shift coding for DC while adaptive RLE, shift optimizer then shift coding applied for AC, the other subbands; LH, HL and HH are compressed using the scalar quantization, Quadtree and shift optimizer then shift coding. In this paper, a new flipping block with an adaptive RLE is proposed and applied for image enhancement. After applying DCT system and scalar quantization, huge number of zeros produced with less number of other values, so an adaptive RLE is used to encode this RUN of zeros which results with more compression.Standard medical images are selected to be used as testing image materials such as CT-Scan, X-Ray, MRI these images are specially used for researches as a testing samples. The results showed high compression ratio with high quality reconstructed images

Evaluation of Lossless Algorithms for Data Compression

“…al., 2017) have used the run length algorithm in order to make it fit within the constraints of FHE execution and then analyzing it to the achieve the optimized FHE execution efficiency. Hybrid DWT-DCT algorithm is applied (Rafea and Salman, 2018) in order to compress the medical image and an adaptive RLE algorithm is used to encode the runs of zero created by the hybrid algorithm in order to achieve better compression ratio results. The authors (Khassaweneh and Alshorman, 2020) have used Frei-chen bases technique for compressing the large image data and then used a modified RLE algorithm in order to enhance the compression factor with out adding any distortion so as to receive the high-quality decompressed image.…”