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A novel lossless image compression algorithm using multiset permutation encoding

International Journal of Information Technology (Singapore) 2025年第1期17卷 97-106页

作者： Auwatanamongkol, Surapong School of Applied Statistics National Institute of Development Administration Bangkok Thailand

In this paper, a new lossless image compression algorithm is proposed. The algorithm is based on the fact that the number of possible permutations for a binary multiset with a fixed number of elements gets smaller when the number of ‘1’ bits in the multiset approaches zero. Therefore, a particular permutation of the multiset can be encoded using few bits if the number of ‘1’ bits in the multiset is small. To enhance the compression performance of the encoding, pixel color transformation is performed as a preprocessing step to eliminate inter-pixel redundancies as well as assign each pixel value a new value that has a number of bits of ‘1’ as few as possible (depending on the frequency of the pixel value in the image). Additional compression techniques such as a variable length encoding and a run length encoding are also incorporated to enhance compression performance of the encoding process. Based on experiments, the performance of the proposed algorithm is comparable to many popular lossless image compression algorithms. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： lossless image compression Permutation encoding Pixel color transformation

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lossless image compression and encryption using SCAN

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PATTERN RECOGNITION 2001年第6期34卷 1229-1245页

作者： Maniccam, SS Bourbakis, NG SUNY Binghamton Image Video Machine Vis Lab Dept Elect Engn Binghamton NY 13902 USA Tech Univ Crete Dept ECE Khania 73100 Crete Greece

This paper presents a new methodology which performs both lossless compression and encryption of binary and gray-scale images. The compression and encryption schemes are based on SCAN patterns generated by the SCAN methodology. The SCAN is a formal language-based two-dimensional spatial-accessing methodology which can efficiently specify and generate a wide range of scanning paths ol space filling curves. This paper presents a brief overview of SCAN, compression and decompression algorithms, encryption and decryption algorithms, and test results of the methodology. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

关键词： lossless image compression image encryption scanning techniques SCAN methodology

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lossless image compression based on integer Discrete Tchebichef Transform

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NEUROCOMPUTING 2016年 214卷 587-593页

作者： Xiao, Bin Lu, Gang Zhang, Yanhong Li, Weisheng Wang, Guoyin Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

Transform coding plays a very important role in image and video compression. Discrete Cosine Transform (DCT) is used as standard scheme (i.e. JPEG) in lossy image compression. Consequently, integer Discrete Cosine Transform (iDCT) is presented to achieve lossless compression for the compatibility of JPEG. Presently, with the investigation of new and well performed image transform techniques, there is an undeniable need for novel transform coding technologies to improve the compression rates and reduce computational complexity in the field of transform based lossless image compression. Discrete Tchebichef Transform (DTT) is a potentially unexploited orthogonal transform, and has shown a number of valuable properties like energy compaction and recursive computation. It has been preliminarily introduced in lossy image compression and shown the superiority in the compression rates. However, the DTT has not been investigated in lossless image compression. In this paper, we study DTT and matrix factorization theory firstly, and then factorize the N x N DTT matrix into N+1 single-row elementary reversible matrices (SERMs) with minimum rounding errors. On this base, we introduce a novel algorithm, named integer DTT (iDTT), to achieve integer to integer mapping for efficient lossless image compression. A series of experiments are carried out and results show that the proposed iDTT algorithm not only has higher compression ratio than iDCT method, but also is compatible with the widely used JPEG standard. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Discrete Tchebichef Transform Discrete cosine transform lossless image compression JPEG Matrix factorization image transform

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lossless image compression by Joint Prediction of Pixel and Context Using Duplex Neural Networks

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IEEE ACCESS 2021年 9卷 86632-86645页

作者： Rhee, Hochang Jang, Yeong Il Kim, Seyun Cho, Nam Ik Seoul Natl Univ INMC Dept Elect & Comp Engn Seoul 08826 South Korea Gauss Labs Inc Seoul 06141 South Korea Seoul Natl Univ Grad Sch Data Sci Seoul 08826 South Korea

This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptive arithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolutional neural networks (CNNs) are also adopted for the lossy/lossless coding. While the existing MLP-based lossless compression schemes focused only on accurate pixel prediction, we jointly predict the pixel values and contexts. We also adopt and design channel-wise progressive learning, residual learning, and duplex network in this MLP-based framework, which leads to improved coding gain compared to the conventional methods. Experiments show that the proposed method performs better than the conventional non-learning algorithms and also recent learning-based compression methods with practical computation time.

关键词： image coding image color analysis Codecs Transforms Predictive models Entropy Quantization (signal) lossless image compression predictive coding neural network adaptive arithmetic coding

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lossless image compression using binary wavelet transform

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IET image PROCESSING 2007年第4期1卷 353-362页

作者： Pan, H. Siu, W. -C. Law, N. -F. Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Multimedia Signal Proc Hong Kong Hong Kong Peoples R China SE Univ Dept Automat Control Engn Nanjing 210018 Peoples R China

A binary wavelet transform (BWT) has several distinct advantages over a real wavelet transform when applied to binary data. No quantisation distortion is introduced and the transform is completely invertible. Since all the operations involved are modulo-2 arithmetic, it is extremely fast. The outstanding qualities of the BWT make it suitable for binary image-processing applications. The BWT, originally designed for binary images, is extended to the lossless compression of grey-level images. An in-place implementation structure of the BWT is explored. Then, a simple embedded lossless BWT-based image-coding algorithm called progressive partitioning binary wavelet-tree coder (PPBWC) is proposed. The proposed algorithm is simple in concept and implementation, but achieves promising lossless compression efficiency as compared with the conventional bitplane scanning methods. Small alphabets in the arithmetic coding, non-causal adaptive context modelling and source division are the major factors that contribute to the gain of compression efficiency of the PPBWC. Experimental results show that the PPBWC outperforms most of other embedded coders in terms of coding efficiency.

关键词： Context modeling Binary images BACKWARD-WAVE TUBES compression EFFICIENCY wavelet transforms ENCODER lossless compression lossless image compression Binary

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lossless image compression: application of Bi-level Burrows Wheeler compression Algorithm (BBWCA) to 2-D data

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第10期76卷 12391-12416页

作者： Khan, Aftab Khan, Ashfaq Khan, Mushtaq Uzair, Muhammad Univ Engn & Technol Peshawar Pakistan Univ Engn & Technol Dept Comp Syst Engn Peshawar Pakistan Univ Engn & Technol Dept Mech Engn Peshawar Pakistan Natl Univ Sci & Technol Dept Design & Mfg Engn SMME Islamabad Pakistan

This research paper demonstrates the robustness of Bi-level Burrows Wheeler compression Algorithm (BBWCA) in terms of the compression efficiency for different types of image data. The scheme was designed to take advantage of the increased inter-pixel redundancies resulting from a two pass Burrows Wheeler Transformation (BWT) stage and the use of Reversible Colour Transform (RCT). In this research work, BBWCA was evaluated for raster map images, Colour Filter Array (CFA) images as well as 2-D ElectroEncephaloGraphy (EEG) data and compared against benchmark schemes. Validation has been carried out on various examples and they show that BBWCA is capable of compressing 2-D data effectively. The proposed method achieves marked improvement over the existing methods in terms of compression size. BBWCA is 18.8 % better at compressing images as compared to High Efficiency Video Codec (HEVC) and 21.2 % more effective than LZ4X compressor for CFA images. For the EEG data, BBWCA is 17 % better at compressing images as compared to WINRK and 25.2 % more effective than NANOZIP compressor. However, for the Raster images PAQ8 supersedes BBWCA by 11 %. Among the different schemes compared, the proposed scheme achieves overall best performance and is well suited to small and large size image data compression. The parallelization process reduces the execution time particularly for large size images. The parallelized BBWCA scheme reduces the execution time by 31.92 % on average as compared to the non-parallelized BBWCA.

关键词： lossless image compression Bi-level BurrowsWheeler compression Algorithm (BBWCA) Reversible Colour Transform(RCT) Colour Filter Array (CFA) images 2-D ElectroEncephaloGraphy (EEG) Colour Space

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lossless image compression Using Super-Spatial Structure Prediction

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IEEE SIGNAL PROCESSING LETTERS 2010年第4期17卷 383-386页

作者： OwenZhao, Xiwen HenryHe, Zhihai Univ Missouri Dept Elect & Comp Engn Columbia MO 65203 USA

We recognize that the key challenge in image compression is to efficiently represent and encode high-frequency image structure components, such as edges, patterns, and textures. In this work, we develop an efficient lossless image compression scheme called super-spatial structure prediction. This super-spatial prediction is motivated by motion prediction in video coding, attempting to find an optimal prediction of structure components within previously encoded image regions. We find that this super-spatial prediction is very efficient for image regions with significant structure components. Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art lossless image compression methods.

关键词： Context-based adaptive lossless image coding (CALIC) lossless image compression structure components super-spatial structure prediction

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A lossless image compression ALGORITHM USING VARIABLE BLOCK SIZE SEGMENTATION

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IEEE TRANSACTIONS ON image PROCESSING 1995年第10期4卷 1396-1406页

作者： RANGANATHAN, N ROMANIUK, SG NAMUDURI, KR NATL UNIV SINGAPORE DEPT INFORMAT SYST & COMP SCISINGAPORE 0511SINGAPORE GTE INFORMAT SERV TAMPAFL 33602

The redundancy in digital image representation can be classified into two categories: local and global, In this paper, we present an analysis of two image characteristics that give rise to local and global redundancy in image representation, Based on this study, we propose a lossless image compression scheme that exploits redundancy both at local and global levels in order to obtain maximum compression efficiency, The proposed algorithm segments the image into variable size blocks and encodes them depending on the characteristics exhibited by the pixels within the block, The proposed algorithm is implemented in software and its performance is better than other lossless compression schemes such as the Huffman, the arithmetic, the Lempel-Ziv and the JPEG.

关键词： lossless image compression JPEG redundancy image representation pixel image compression algorithm variable block algorithms lossless compression

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lossless image compression by LMS adaptive filter banks

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SIGNAL PROCESSING 2001年第2期81卷 447-450页

作者： Öktem, R Çetin, AE Gerek, ON Öktem, L Egiazarian, K Bilkent Univ Dept Elect & Elect Engn TR-06533 Bilkent Ankara Turkey

A lossless image compression algorithm based on adaptive subband decomposition is proposed. The subband decomposition is achieved by a two-channel LMS adaptive filter bank. The resulting coefficients are lossy coded first, and then the residual error between the lossy and error-free coefficients is compressed. The locations and the magnitudes of the nonzero coefficients are encoded separately by an hierarchical enumerative coding method. The locations of the nonzero coefficients in children bands are predicted from those in the parent band. The proposed compression algorithm, on the average, provides higher compression ratios than the state-of-the-art methods. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： adaptive subband decomposition lossless image compression LMS algorithm

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lossless image compression using ordered binary-decision diagrams

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ELECTRONICS LETTERS 2001年第3期37卷 162-163页

作者： Mateu-Villarroya, P Prades-Nebot, J Univ Politecn Valencia Dept Comun ETSITV Valencia 46022 Spain

A lossless compression algorithm for images based on ordered binary-decision diagrams (OBDDs) is presented. The algorithm finds an OBDD which represents the image exactly and then codes the OBDD efficiently. The resul... 详细信息

关键词： OBDD binary decision diagrams image and video coding data compression image coding Signal processing theory lossless compression algorithm Combinatorial mathematics lossless image compression Computer vision and image processing techniques ordered BDD ordered binary-decision diagrams

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