A hybrid predictive technique for lossless image compression

Abstract

Compression of images is of great interest in applications where efficiency with respect to data storage or transmission bandwidth is sought.The rapid growth of social media and digital networks have given rise to huge amount of image data being accessed and exchanged daily. However, the larger the image size, the longer it takes to transmit and archive. In other words, high quality images require huge amount of transmission bandwidth and storage space. Suitable image compression can help in reducing the image size and improving transmission speed. Lossless image compression is especially crucial in fields such as remote sensing healthcare network, security and military applications as the quality of images needs to be maintained to avoid any errors during analysis or diagnosis. In this paper, a hybrid prediction lossless image compression algorithm is proposed to address these issues. The algorithm is achieved by combining predictive Differential Pulse Code Modulation (DPCM) and Integer Wavelet Transform (IWT). Entropy and compression ratio calculation are used to analyze the performance of the designed coding. The analysis shows that the best hybrid predictive algorithm is the sequence of DPCM-IWT-Huffman which has bits sizes reduced by 36%, 48%, 34% and 13% for tested images of Lena, Cameraman, Pepper and Baboon, respectively.

Improved Median Edge Detection (iMED) for Lossless Image Compression

“…The proposed algorithm for lossless image compression (Azman et al, 2019) can achieve the desired results by combining Integer Wavelet Transform (IWT) and Differential Pulse Code Modulation (DPCM). To analyze the performance of this hybrid algorithm, two parameters, i.e., compression ratio and entropy, are used.…”

Pixel Based Techniques for Gray Image Compression: A review

“…The experimental results were tested using five medical images, the attained CR between 4.3 to 7.8, with PSNR values between 50.2dB to 54.3dB. [21], proposed a hybrid lossless grayscale image compression system that combination of DPCM (third order, 2D structure, Casual model) of spatial based techniques along lifting scheme and Haar DWT of frequency-based techniques followed by entropy coding of Huffman coding. The system was tested on five standard grayscale square images of sizes 512x512 pixels, where CR was achieved between 1.025 to 2.046... [22], exploited two linear prediction models pixel-based to compress grayscale images losslessly using arithmetic coding, where the first model used two prediction forms, one of pixel intensity in Upper-Left neighbor / Upper-Right neighbor (UL/UR), the other one used Upper(U), Left(L), Right(R), Bottom(B) neighbors, while the second model used three prediction forms of one, four neighbors, where the former used the left bottom, the other used either UL, UR, Bottom Left-neighbor (BL), Bottom-Left neighbor (BR) or U,L, R,B.…”

A spatial image compression algorithm based on run length encoding

“…Their scheme is tested on some sample images and got better results than SPIHT at the sharp edges in the decompressed image. Azman authors in [31] have combined predictive differential pulse code modulation (DPCM) and integer wavelet transform (IWT) in order to achieve a hybrid prediction lossless image compression approach. They have analyzed the performance of their proposed algorithm by calculating the entropy and the compression ratio.…”