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29th Picture coding Symposium (PCS)

作者： Ulacha, Grzegorz Stasinski, Ryszard West Pomeranian Univ Technol Dept Comp Sci Szczecin Poland Poznan Univ Tech Fac Elect & Commun Poznan Poland

ISBN: (纸本)9781457720482

In the paper optimization of lossless image coders based on Adaptive Neural Networks (AdNN) is addressed. Firstly, a detailed analysis of the influence of AdNN parameters on coder performance (average bitrate, time complexity) is done. Secondly, an improved technique denoted AdNN(+) is proposed. Its main features are introduction of contexts, variable training window size, and post-processing by NLMS algorithm. Experiments show that indeed, the new method is better than others based on neural networks, and that it can even compete with the best existing image lossless coding algorithms.

关键词： lossless image coding adaptive neural networks contexts arithmetic coder NLMS

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A new simple context lossless image coding algorithm based on adaptive context arithmetic coder

A new simple context lossless image coding algorithm based o...

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15th International Conference on Systems, Signals and image Processing

作者： Ulacha, G. Stasinski, R. Tech Univ Szczecin Dept Comp Sci Zolnierska 49 PL-71210 Szczecin Poland Tech Univ Szczecin Fac Elect & Commun PL-71210 Szczecin Poland Poznan Univ Tech PL-60965 Poznan Poland

ISBN: (纸本)9788022728560

In the paper an effective context based lossless coding technique is presented. Three principal and one auxiliary contexts are defined. Cumulated predictor error combining 8 bias estimators is calculated. An adaptive context arithmetic coder is introduced. It is shown experimentally that the new technique is time-effective while it outperforms the well known methods having reasonable time complexity, and is inferior only to extremely computationally complex ones. http://***/stamp/***?tp=&arnumber=4604363

关键词： lossless image coding data modeling context coding cumulated predictor error arithmetic coder

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A New Simple Context lossless image coding Algorithm Based on Adaptive Context Arithmetic Coder

A New Simple Context Lossless Image Coding Algorithm Based o...

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International Conference on Systems, Signals and image Processing

作者： G. Ulacha R. Stasinski Department of Computer Science and Faculty of Electronics and Communications Szczecin University of Technology and Poznan University of Technology Zolnierska 49 71-210 Szczecin Poland and Polanka 3 60-965 Poznan Poland

ISBN: (纸本)9788022728560

关键词： lossless image coding Data modeling Context coding Cumulated predictor error Arithmetic coder lossless image coding complexity classes Data modelling ENCODER arithmetic

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LIFTING-BASED PARAUNITARY FILTERBANKS FOR LOSSY/lossless image coding

LIFTING-BASED PARAUNITARY FILTERBANKS FOR LOSSY/LOSSLESS IMA...

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European Signal Processing Conference

作者： Taizo Suzuki Yuichi Tanaka Masaaki Ikehara Keio University Department of Electronics and Electrical Engineering Yokohama Kanagawa 223-8522 Japan email: {suzuki ytanaka}@tkhm.elec.keio.ac.jp

This paper introduces one of the image transform methods using M-channel paraunitary filterbanks (PUFBs) based on Householder matirx. First, redundant parameters of PUFB are eliminated by using the fact that they can be factorized into Givens rotation matices. Next, we propose an eliminating redundant parameters method based on Householder matrix using relationship between Givens rotation and Householder matrices. In addition, PUFBs are factorized into the lifting structure for lossless image coding, and we call them as lifting-based PUFBs (LBPUFBs). LBPUFBs based on Householder matrix have less number of rounding operators than Givens rotation matrix version, since proposed structure is efficiency for lossless image coding. Finally, we show some exsamples to validate our method in lossy/lossless image coding.

关键词： Paraunitary filterbank householder matrix redundant parameters lifting structure lossless image coding

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Highly Effective Predictor Blending Method for lossless image coding

Highly Effective Predictor Blending Method for Lossless Imag...

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IEEE Mediterranean Electrotechnical Conference

作者： Grzegorz Ulacha Ryszard Stasinski Faculty of Computer Science and Information Systems West Pomeranian University of Technology Faculty of Electronics and Communications Poznan University of Technology

ISBN: (纸本)9781424457939

A fast and effective predictor blending method is described in the paper. The special set of 17 blended predictors is defined, including GAP_+, and (novel) texture context mapping one. An advanced error bias removal method is applied to some subpredictors. A sophisticated adaptive context arithmetic coder forms the final stage of the algorithm. Experimental results show that indeed, for considered execution times (few seconds for Lennagrey image) the presented technique is better than other state-of-the-art methods.

关键词： lossless image coding ENCODER Blending

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lossless image compression via predictive coding of discrete Radon projections

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SIGNAL PROCESSING-image COMMUNICATION 2008年第4期23卷 313-324页

作者： Kingston, Andrew Autrusseau, Florent Polytech Nantes IRCCyN Lab F-44306 Nantes France Australian Natl Univ Res Sch Phys Sci & Engn Dept Appl Math Canberra ACT 0200 Australia

This paper investigates predictive coding methods to compress images represented in the Radon domain as a set of projections. Both the correlation within and between discrete Radon projections at similar angles can be exploited to achieve lossless compression, The discrete Radon projections investigated here are those used to define the Mojette transform first presented by Guedon et al. [Psychovisual image coding via an exact discrete Radon transform, in: T.W. Lance (Ed.), Proceedings of the Visual Communications AND image Processing (VCIP), May 1995, Taipei, Taiwan, pp. 562-572]. This work is further to the preliminary investigation presented by Autrusseau et al. [lossless compression based on a discrete and exact radon transform: a preliminary study, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. II, May 2006, Toulouse, France, pp. 425-428]. The I D Mojette projections are re-arranged as two dimensional images, thus allowing the use of 2D image compression techniques onto the projections. Besides the compression capabilities, the Mojette transforms brings an interesting property: a tunable redundancy. As the Mojette transform is able to both compress and add redundancy, the proposed method can be viewed as a joint lossless source-channel coding technique for images. We present here the evolution of the compression ratio depending on the chosen redundancy. (c) 2008 Elsevier B.V. All rights reserved.

关键词： lossless image coding discrete Radon transform Mojette redundancy

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Initial Bits Generation for lossless image Compression Using Bits-Back coding with a Latent Variable Model

Initial Bits Generation for Lossless Image Compression Using...

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International Workshop on Advanced Imaging Technology (IWAIT)

作者： Sugiyama, Haruka Suzuki, Kaiyu Matsuda, Ichiro Tokyo Univ Sci Dept Elect Engn 2641 Yamazaki Noda Chiba 2788510 Japan

ISBN: (数字)9781510679931

ISBN: (纸本)9781510679931;9781510679924

Bits-back coding is a kind of entropy coding algorithm and can be used with an image generative model using latent variables for lossless image compression. It achieves excellent coding performance when ignoring the amount of initial bits which must be prepared independent of image contents. However, the initial bits are considered as additional information and indispensable for the encoding process. In this paper, a way of initial bits generation as well as internal precision of the latent variables are investigated for better coding performance in a practical sense.

关键词： lossless image coding bits-back coding latent variable model diffusion model

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Paths to Future image lossless coding

Paths to Future Image Lossless Coding

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54th ELMAR International Symposium

作者： Ulacha, Grzegorz Stasinski, Ryszard West Pomeranian Univ Technol Dept Comp Sci Szczecin Poland Poznan Univ Tech Fac Elect & Commun Poznan Poland

ISBN: (纸本)9789537044138

In the paper introduction of non-MMSE (minimum mean square error) based predictor optimization techniques to adaptive lossless image compression is considered. The proposed cost function is Minkowski distance, being generalization of MMSE and MMAE (minimum mean absolute error). It is proven empirically and explained why MMSE criterion is suboptimal. Then, currently the best lossless image coding methods are outlined (AVE-WLS, Blend-20), and introduction of new criteria to their definitions discussed.

关键词： lossless image coding MMSE MMAE Minkowski distance Adaptive Predictors Predictor Blending

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An Integrated Approach for lossless image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic coding

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SN Computer Science 2024年第5期5卷 523页

作者： Kumar, P. R. Rajesh Prabhakar, M. School of Computer Science and Engineering REVA University Bangalore India

lossless image compression techniques play a crucial role in preserving image quality while reducing storage space and transmission bandwidth. This paper proposes a novel hybrid integrated method for lossless image compression by combining Contrast Limited Adaptive Histogram Equalization (CLAHE), two-channel encoding, and adaptive arithmetic coding to achieve highly efficient compression without any loss of image information. The first step of the proposed approach involves applying Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the local contrast of the image. This pre-processing step aids in reducing the entropy and increasing the redundancy in the image, creating a more favourable environment for subsequent compression algorithms. Next, the image is divided into two channels: one channel focuses on encoding essential structural information, while the other channel handles the finer details. This segregation leverages the inherent properties of images to improve compression efficiency. To achieve further compression gains, an adaptive arithmetic coding algorithm for encoding the data in each channel is utilized. Adaptive arithmetic coding adapts its probability model during the encoding process, leading to improved compression performance compared to traditional static coding methods. The proposed method offers significant potential in various applications, it is especially crucial in medical imaging, where large volumes of high-resolution images are generated during procedures such as MRI, CT scans, or digital pathology, transmitting high-quality images in resource-constrained environments, and facilitating image processing tasks requiring precise data preservation. CLAHE can be a valuable tool in medical imaging to enhance essential diagnostic information in medical images before compression. By improving contrast and visibility of structures, CLAHE may aid in achieving better compression efficiency and reduce the risk of introducing compres

关键词： Arithmetic coding Bit rate CLAHE coding efficiency Compression ratio lossless image coding Pre-processing Two-channel coding

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lossless coding OF MULTIMODAL image PAIRS BASED ON image-TO-image TRANSLATION 10

LOSSLESS CODING OF MULTIMODAL IMAGE PAIRS BASED ON IMAGE-TO-...

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10th European Workshop on Visual Information Processing (EUVIP)

作者： Parracho, Joao O. Thomaz, Lucas A. Tavora, Luis M. N. Assuncao, Pedro A. A. Faria, Sergio M. M. Inst Telecomunicacoes Leiria Portugal Univ Fed Rio De Janeiro UFRJ Rio De Janeiro Brazil Polytechn Leiria ESTG Leiria Portugal

ISBN: (数字)9781665466233

ISBN: (纸本)9781665466233

Multimodal image coding often uses standard encoding algorithms, which do not exploit multimodality characteristics. This paper proposes a new cross-modality prediction approach for lossless coding of multimodal images, based on a Generative Adversarial Network (GAN). The GAN is added to the prediction loop of the Versatile Video coding (VVC) lossless encoder to perform cross-modality translation of an image to its counterpart modality. Then, such synthesized image is used as reference for inter prediction, followed by further optimization that includes rescaling and brightness adjustment. A publicly available dataset of Positron Emission Tomography (PET) and Computed Tomography (CT) image pairs is used to assess the performance of the proposed multimodal lossless image coding framework. In comparison with single modality coding using the VVC standard, average coding gains of 6.83% are achieved for the inter-coded PET images.

关键词： lossless image coding Multimodal image coding Learning based prediction Generative predictive coding Versatile Video coding

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