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Lossless Compression Framework Using Lossy Prior for High-Resolution Remote Sensing Images

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 8590-8601页

作者： Gu, Enjia Zhang, Yongshan Wang, Xinxin Jiang, Xinwei China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China

Lossless compression of remote sensing images is critically important for minimizing storage requirements while preserving the complete integrity of the data. The main challenge in lossless compression lies in striking a good balance between reasonable compression durations and high compression ratios. In this article, we introduce an innovative lossless compression framework that uniquely utilizes lossy compression data as prior knowledge to enhance the compression process. Our framework employs a checkerboard segmentation technique to divides the original remote sensing image into various subimages. The main diagonal subimages are compressed using a traditional lossy method to obtain prior knowledge for facilitating the compression of all subimages. These subimages are then subjected to lossless compression using our newly developed lossy prior probability prediction network (LP3Net) and arithmetic coding in a specific order. The proposed LP3Net is an advanced network architecture, consisting of an image preprocessing module, a channel enhancement module, and a pixel probability transformer module, to learn the discrete probability distribution of each pixel within every subimage, enhancing the accuracy and efficiency of the compression process. Experiments on high-resolution remote sensing image datasets demonstrate the effectiveness and efficiency of the proposed LP3Net and lossless compression framework, achieving a minimum of 4.57% improvement over traditional compression methods and 1.86% improvement over deep learning-based compression methods.

关键词： Image coding Remote sensing Image segmentation arithmetic Transform coding Transformers Redundancy Accuracy Probability distribution Hyperspectral imaging arithmetic coding checkerboard segmentation discrete probability prediction JPEG XL resampling lossless remote sensing image compression

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Joint source/channel coding and MAP decoding of arithmetic codes

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IEEE TRANSACTIONS ON COMMUNICATIONS 2005年第6期53卷 1007-1016页

作者： Grangetto, M Cosman, P Olmo, G Politecn Torino Ctr Multimedia Commun I-10129 Turin Italy Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

In this paper, a novel maximum a posteriori (MAP) estimation approach is employed for error correction of arithmetic codes with a forbidden symbol. The system is founded on the principle of joint source channel coding, which allows one to unify the arithmetic decoding and error correction tasks into a single process, with superior performance compared to traditional separated techniques. The proposed system improves the performance in terms of error correction with respect to a separated source and channel coding approach based on convolutional codes, with the additional great advantage of allowing complete flexibility in adjusting the coding rate. The proposed MAP decoder is tested in the case of image transmission across the additive white Gaussian noise channel and compared against standard forward error correction techniques in terms of performance and complexity. Both hard and soft decoding are taken into account, and excellent results in terms of packet error rate and decoded image quality are obtained.

关键词： arithmetic coding image transmission joint source/channel coding (JSCC) maximum a posteriori (MAP) estimation

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Maximum a Posteriori Decoding of arithmetic Codes in Joint Source-Channel coding

Maximum a Posteriori Decoding of Arithmetic Codes in Joint S...

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7th International Joint Conference on e-Business and Telecommunications (ICETE)

作者： Spiteri, Trevor Buttigieg, Victor Univ Malta Msida 2080 Msd Malta

ISBN: (纸本)9783642252051

arithmetic codes are being increasingly used in the entropy coding stage in many multimedia transmission applications. Combining channel coding with arithmetic coding can give implementation and performance advantages compared to separate source and channel coding. In this work, novel improvements are introduced into a technique by Grangetto et al. that uses maximum a posteriori (MAP) estimation for decoding joint source-channel coding using arithmetic codes. The arithmetic decoder is modified for quicker symbol decoding and error detection by the introduction of a look-ahead technique. and the calculation of the MAP metric is modified for faster error detection. These modifications also result in improved performance compared to the original scheme. Experimental results show an improvement of up to 0.4 dB when using soft-decision decoding and 0.6 dB when using hard-decision decoding.

关键词： arithmetic coding Joint source-channel coding Maximum a posteriori decoding

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Joint Source-Channel coding using Finite State Integer arithmetic Codes

Joint Source-Channel Coding using Finite State Integer Arith...

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IEEE International Conference on Electro/Information Technology

作者： Moradmand, Hashem Payandeh, Ali Aref, Mohammad Reza MUT Elect & Elect Engn Ctr Tehran Iran Sharif Univ Technol Tehran Iran

ISBN: (纸本)9781424433544

Inserting redundancy to arithmetic codes is a common strategy to add error detection capability to this well-known family of source codes. By using this strategy error correction is possible through some decoding algorithms such as Viterbi decoder. In this paper a system has proposed that uses finite state integer arithmetic codes (FSAC) as a joint source-channel code in combination with a cyclic redundancy check (CRC) and a List Viterbi decoder. The proposed scheme has shown better performance than previous ones.

关键词： joint source channel coding arithmetic coding compression finite state machine list Viterbi decoder

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Enhanced Dual Reversible Data Hiding Using Combined Approaches

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APPLIED SCIENCES-BASEL 2025年第6期15卷 3279-3279页

作者： Kim, Cheonshik Yang, Ching-Nung Leng, Lu Sejong Univ Dept Comp Engn Seoul 05006 South Korea Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Hualien 97401 Taiwan Nanchang Hangkong Univ Sch Software Nanchang 330063 Peoples R China

This paper proposes a reversible data hiding technique based on two cover images. The proposed method enhances performance by utilizing Hamming coding (HC), arithmetic coding (AC), and an improved Exploiting Modification Direction (EMD) technique. Since AC provides lossless compression for binary data, it is widely used in image compression and helps maximize the efficiency of data transmission and storage. The EMD technique is recognized as an efficient data hiding method. However, it has a significant limitation: it does not allow for the restoration of the original cover image after data extraction. Additionally, EMD has a data hiding capacity limit of approximately 1.2 bpp. To address these limitations, an improved reversible data hiding technique is proposed. In this study, HC and AC are integrated with an improved EMD technique to enhance data hiding performance, achieving higher embedding capacity while ensuring the complete restoration of the original cover image. In the proposed method, Hamming coding is applied for data encoding and arithmetic coding is used for compression to increase efficiency. The compressed data are then embedded using the improved EMD technique, enabling the receiver to fully restore the original cover image. Experimental results demonstrate that the proposed method achieves an average PSNR of 66 dB and a data embedding capacity of 1.5 bpp, proving to be a promising approach for secure and efficient data hiding applications.

关键词： data hiding (DH) reversible DH (RDH) dual RDH Hamming coding arithmetic coding exploiting modification direction (EMD)

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Analysis of Bit-Plane Probability for Generalized Gaussian Distribution and its Application in Audio coding

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2012年第4期20卷 1167-1176页

作者： Shu, Haiyan Huang, Haibin Rahardja, Susanto ASTAR Inst Infocomm Res Singapore 138632 Singapore

Bit-plane probability of data is useful information for many applications such as entropy coding and rate estimation, particularly for scalable coding system. Typically, the value of bit-plane probability varies according to the distribution of the data represented, and due to inter-plane correlation, it is hard to get an analytic solution of bit-plane probability for the data with generalized Gaussian distribution. In this paper, bit-plane probability is analyzed when Generalized Gaussian distribution is used to model the input data. Based on the study of bit-plane probability for Laplace distribution and the relationship between different bit-planes, an approximated bit-plane probability for Generalized Gaussian distribution is presented. This closed-form expression is of low computational cost. Furthermore, a much practical format is derived with reduced complexity for implementation in the state-of-the-art MPEG-4 scalable to lossless audio coding system. With the same computational cost, the proposed algorithm presents higher compression efficiency than MPEG-4 scalable to lossless audio coding, which considers Laplace distribution only.

关键词： arithmetic coding bit-plane probability generalized Gaussian distribution

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GLS coding based security solution to JPEG with the structure of aggregated compression and encryption

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2014年第5期19卷 1366-1374页

作者： Zhang, Yushu Xiao, Di Liu, Hong Nan, Hai Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Coll Software Engn Chongqing 400065 Peoples R China

There exists close relation among chaos, coding and cryptography. All the three can be combined into a whole as aggregated chaos-based coding and cryptography (ATC) to compress and encrypt data simultaneously. In particular, image data own high redundancy and wide transmission and thereby it is well worth doing research on ATC for image, which is very helpful to real application. JPEG with high compression ratio has not provided security. If JPEG is incorporated into powerful cryptographic features, its application can be further extended. For this reason, in this paper, GLS coding as a special form of ATC, which attains synchronous compression and encryption, is used to modify JPEG and fill its gap. An image is first initialized using DCT, quantization and run-length coding in turn, just as JPEG. Then, it is encoded and encrypted simultaneously by utilizing GLS coding and binary keystream resulting from the chaotic generator. Results demonstrate that our scheme can not only achieve good compression performance but also resist known/chosen-plaintext attacks efficiently. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Aggregated chaos-based compression and encryption GLS arithmetic coding DCT Quantization Run-length coding

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Weighted forward looking adaptive coding

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THEORETICAL COMPUTER SCIENCE 2022年 930卷 86-99页

作者： Fruchtman, Aharon Gross, Yoav Klein, Shmuel T. Shapira, Dana Ariel Univ Dept Comp Sci IL-40700 Ariel Israel Bar Ilan Univ Dept Comp Sci IL-52900 Ramat Gan Israel

Huffman coding is known to be optimal under certain constraints, yet its dynamic version, which constantly alters the Huffman tree as a function of the already processed characters, may be even more efficient in practice. A new forward looking variant of Huffman compression has been proposed recently, that provably always performs better than static Huffman coding by at least m -1 bits, where m denotes the size of the alphabet, and has a better worst case size than the standard dynamic Huffman coding. This paper introduces a new generic coding method, extending the known static and dynamic variants and including them as special cases. In fact, the generalization is applicable to all statistical methods, including arithmetic coding. This leads then to the formalization of a new double -pass coding method that is adaptive in the sense that it uses changing statistics depending on the current position within the processed file, yet it behaves like static coding, as it assumes the knowledge of the distribution in the entire file;this is contrary to online variants that rely only on the text seen so far and adapt the model dynamically. We call the new method positional coding, and its compression performance, using global statistics, is provably always at least as good as that of the best dynamic variants known to date. Moreover, we present empirical results that show improvements by positional coding and its extensions over static and dynamic Huffman and arithmetic coding, even when the encoded file includes the model description.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Lossless data compression Static and dynamic coding Huffman coding arithmetic coding

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A new chain coding mechanism for compression stimulated by a virtual environment of a predator-prey ecosystem

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 102卷 650-669页

作者： Dhou, Khaldoon Drury Univ Breech Sch Business Adm Springfield MO 65802 USA

In this paper, the researcher introduces a new chain coding mechanism that employs a predator-prey agent-based modeling simulation with some modifications and simplifications and uses it in compression. In the proposed method, an image is ultimately represented by a virtual world consisting of three agent types: wolves, sheep, and paths. While sheep and paths do not change their locations during the program execution, wolves search for sheep to prey on. With each step, a wolf can determine its path by choosing between seven pertinent moves depending on the encountered information. The algorithm keeps track of the wolves' initial locations and their movements and uses this as a new image representation. Additionally, the researcher introduces the 'Lengthy Advance Move,' the purpose of which is to group particular consecutive codes and further reduce the chain. This, in turn, allows the researcher to experiment with different variations of the algorithm. Finally, the researcher applies arithmetic coding on the series of movements for extra compression. The experimental results reveal that the current algorithm generates higher compression ratios than many standardized algorithms, including JBIG family algorithms. Most importantly, paired-sample t-tests reveal significant differences between the findings of the wolf-sheep predation algorithm and the other algorithms used as benchmarks for comparisons. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Agent-based modeling Chain code Bi-level image Compression arithmetic coding Predator-prey ecosystem

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A prediction-based lossless image compression procedure using dimension reduction and Huffman coding

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第3期82卷 4081-4105页

作者： Rahman, Md Atiqur Hamada, Mohamed Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

Advanced therapeutic imaging innovation produces an immense amount of information, predominantly from processed tomography and other imaging modalities. This causes a significant challenge when storing them on a local personal computer or communicating them over cyberspace. Therefore, a proficient image compression system is fundamentally required. From this perspective, this paper proposes a lossless image compression procedure by reducing image dimension and using a prediction technique. In the proposed strategy, the column dimension of a grey-scale image is first reduced and then the prediction errors are encoded using Huffman coding. The decoding process is carried out in the reverse direction. The proposed method is executed and applied to several bench-marked images. The performance of this proposed algorithm is assessed and compared with the state-of-the-art techniques based on several assessment criteria, such as average code length (ACL), compression ratio (CR), encoding time, decoding time, efficiency, peak signal to noise ratio (PSNR) and normalised correlation (NC). The proposed algorithm also demonstrates an improvement in the average code length compared with the state-of-the-art techniques.

关键词： Run-length Huffman arithmetic coding Lossless JPEG JPEG 2000 CALIC JPEG XR JPEG-LS Compression ratio

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