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Multiplierless lifting-based fast X transforms derived from fast Hartley transform factorization

MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2018年第1期29卷 99-118页

作者： Suzuki, Taizo Kyochi, Seisuke Tanaka, Yuichi Ikehara, Masaaki Univ Tsukuba Fac Engn Informat & Syst Tsukuba Ibaraki Japan Univ Kitakyushu Dept Informat & Media Engn Kitakyushu Fukuoka Japan Tokyo Univ Agr & Technol Grad Sch BASE Koganei Tokyo Japan Keio Univ Dept Elect & Elect Engn Yokohama Kanagawa Japan

This paper presents M-channel (M = 2(N), N is an element of N, N >= 1) multiplierless liftingbased (ML-) fast X transforms (FXTs), where X = F (Fourier), C (cosine), S (sine), and H (Hartley), i. e., FFT, FCT, FST, and FHT, derived from FHT factorization as way of lowering the cost of signal (image) processing. The basic forms of ML-FXTs are described. Then, they are customized for efficient image processing. The customized ML-FFT has a real-valued calculation followed by a complex-valued one. The ML-FCT customization for a block size of 8, which is a typical size for image coding, further reduces computational costs. We produce two customized ML-FCTs for lossy and lossless image coding. Numerical simulations show that ML-FFT and ML-FCTs perform comparably to the conventional methods in spite of having fewer operations.

关键词： factorization image processing lossless image coding Fasting Transform Hartley transform

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Adaptive prediction for lossless image compression

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SIGNAL PROCESSING-image COMMUNICATION 2002年第5期17卷 363-372页

作者： Marusic, S Deng, G La Trobe Univ Dept Elect Engn Bundoora Vic 3086 Australia

lossless image compression is often performed through decorrelation, context modelling and entropy coding of the prediction error. This paper aims to identify the potential improvements to compression performance through improved decorrelation. Two adaptive prediction schemes are presented that aim to provide the highest possible decorrelation of the prediction error data. Consequently, complexity is overlooked and a high degree of adaptivity is sought. The adaptation of the respective predictor coefficients is based on training of the predictors in a local causal area adjacent to the pixel to be predicted. The causal nature of the training means no transmission overhead is required and also enables lossless coding of the images. The first scheme is an adaptive neural network, trained on the actual data being coded enabling continuous updates of the network weights. This results in a highly adaptive predictor, with localised optimisation based on stochastic gradient learning. Training for the second scheme is based on the recursive LMS (RLMS) algorithm incorporating feedback of the prediction error. In addition to the adaptive prediction, the results presented here also incorporate an arithmetic coding scheme, producing results which are better than CALIC. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： adaptive prediction lossless image coding recursive LMS (RLMS) adaptive neural network

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Why does histogram packing improve lossless compression rates?

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IEEE SIGNAL PROCESSING LETTERS 2002年第8期9卷 259-261页

作者： Ferreira, PJSG Pinho, AJ Univ Aveiro Dept Elect & Telecomunicacoes P-3810193 Aveiro Portugal Univ Aveiro Inst Elect Engn P-3810193 Aveiro Portugal

The performance of state-of-the-art lossless image coding methods [such as JPEG-LS, lossless JPEG-2600, and context-based adaptive lossless image coding (CALIC)] can be considerably improved by a recently introduced preprocessing technique that can be applied whenever the images have sparse histograms. Bitrate savings of up to 50% have been reported, but so far no theoretical explanation of the fact has been advanced. This letter addresses this issue and analyzes the effect of the technique in terms of the interplay between histogram packing and the image total variation, emphasizing the lossless JPEG-2000 case.

关键词： context-based adaptive lossless image coding (CALIC) histogram packing image variation JPEG-2000 JPEG-LS linear approximation lossless image coding nonlinear approximation

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PIXEL PREDICTION BY CONTEXT BASED REGRESSION

PIXEL PREDICTION BY CONTEXT BASED REGRESSION

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sheng, Lingyan Ortega, Antonio Univ So Calif Dept Elect Engn Viterbi Sch Engn Signal & Image Proc Inst Los Angeles CA 90089 USA

ISBN: (纸本)9781467300469

We propose a pixel prediction algorithm, which learns a regression function corresponding to each context. A context refers to a group of pixels, that have similar correlations with its neighboring pixels. We propose to form a pixel's feature vector by its neighboring pixels' ratios, so that they better capture the pixel properties described by the regression weights. Then we use K-means clustering to classify the feature vectors of all pixels into several contexts. Clustering reduces pixel randomness within each context, thus reducing prediction error. We apply three regression algorithms, the least square, quantile and lasso regression, which assume different loss function and regularization. Experimental results demonstrate that all context based regression methods have outperformed conventional pixel predictors. Among them, quantile regression, which assumes l1-norm loss function has the best result. It has 3.1% less bits per pixel (bpp) than least square prediction with 12 neighboring pixels.

关键词： lossless image coding least square quantile regression lasso regression

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NON-SEPARABLE WAVELET TRANSFORM USING LEARNABLE CONVOLUTIONAL LIFTING STEPS 31

NON-SEPARABLE WAVELET TRANSFORM USING LEARNABLE CONVOLUTIONA...

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2024 International Conference on image Processing

作者： Parracho, Joao O. da Silva, Eduardo A. B. Thomaz, Lucas A. Tavora, Luis M. N. Faria, Sergio M. M. Inst Telecomun Leiria Portugal Fed Univ Rio Janeiro Elect Engn Program COPPE Rio De Janeiro Brazil Polytech Leiria ESTG Leiria Portugal

ISBN: (纸本)9798350349405;9798350349399

Wavelet transforms have been a relevant topic in signal processing for many years. One of the most common strategies when designing wavelet transforms is the use of lifting schemes, known for their perfect reconstruction properties and flexible design. This paper introduces a novel 2D non-separable lifting design methodology based on deep learning architectures. The proposed method is assessed within the context of end-to-end lossless image compression. The attained results highlight a significant 99% reduction of the number of learnable coefficients, with a direct impact on the computational complexity, while still achieving higher compression performance when compared to other end-to-end state-of-the-art wavelet-based frameworks. The code for the models used to obtain the reported results are available at https://***/joaoparracho/2D-NSWT-LCLS.

关键词： Wavelet transforms Non-separable lifting Convolutional neural networks lossless image coding

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NONLINEAR PREDICTION OF MULTIDIMENSIONAL SIGNALS VIA DEEP REGRESSION WITH APPLICATIONS TO image coding 44

NONLINEAR PREDICTION OF MULTIDIMENSIONAL SIGNALS VIA DEEP RE...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Zhang, Xi Wu, Xiaolin Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

ISBN: (纸本)9781479981311

Deep convolutional neural networks (DCNN) have enjoyed great successes in many signal processing applications because they can learn complex, non-linear causal relationships from input to output. In this light, DCNNs are well suited for the task of sequential prediction of multidimensional signals, such as images, and have the potential of improving the performance of traditional linear predictors. In this research we investigate how far DCNNs can push the envelop in terms of prediction precision. We propose, in a case study, a two-stage deep regression DCNN framework for nonlinear prediction of two-dimensional image signals. In the first-stage regression, the proposed deep prediction network (PredNet) takes the causal context as input and emits a prediction of the present pixel. Three PredNets are trained with the regression objectives of minimizing l(1), l(2) and l(infinity) norms of prediction residuals, respectively. The second-stage regression combines the outputs of the three PredNets to generate an even more precise and robust prediction. The proposed deep regression model is applied to lossless predictive image coding, and it outperforms the state-of-the-art linear predictors by appreciable margin.

关键词： Deep regression nonlinear prediction lossless image coding

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Efficient lossless coding of Highpass Bands from Block-based Motion Compensated Wavelet Lifting Using JPEG 2000

Efficient Lossless Coding of Highpass Bands from Block-based...

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IEEE Visual Communications and image Processing Conference

作者： Schnurrer, Wolfgang Troeger, Tobias Richter, Thomas Seiler, Juergen Kaup, Andre Univ Erlangen Nurnberg Multimedia Commun & Signal Proc Cauerstr 7 D-91058 Erlangen Germany

ISBN: (纸本)9781479961399

lossless image coding is a crucial task especially in the medical area, e.g., for volumes from Computed Tomography or Magnetic Resonance Tomography. Besides lossless coding, compensated wavelet lifting offers a scalable representation of such huge volumes. While compensation methods increase the details in the lowpass band, they also vary the characteristics of the wavelet coefficients, so an adaption of the coefficient coder should be considered. We propose a simple invertible extension for JPEG 2000 that can reduce the filesize for lossless coding of the highpass band by 0.8% on average with peak rate saving of 1.1%.

关键词： Computed Tomography Wavelet Lifting Signal Analysis Adaptive coding lossless image coding

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Predictor Blending for Real-Time lossless coding System

Predictor Blending for Real-Time Lossless Coding System

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50th International Symposium ELMAR

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

ISBN: (纸本)9789537044060

In the paper modeling stage of a simple and efficient lossless image coding technique intended for use in real-time hardware coding system is described The method is based on predictors blending approach, which is the kernel of some the best current lossless coding methods. Additionally, prediction blending is well suited for parallel implementations. The predictor blending is combined with context coding, and bias removal algorithm, the latter one implements both CALIC, and LOCO-I approaches. Experiments show that indeed, the new technique has better performance than the widely known older methods.

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

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On the Influence of Clipping in lossless Predictive and Wavelet coding of Noisy images

On the Influence of Clipping in Lossless Predictive and Wave...

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29th Picture coding Symposium (PCS)

作者： Schnurrer, Wolfgang Seiler, Juergen Schoeberl, Michael Kaup, Andre Univ Erlangen Nurnberg D-91058 Erlangen Germany

ISBN: (纸本)9781457720482

Especially in lossless image coding the obtainable compression ratio strongly depends on the amount of noise included in the data as all noise has to be coded, too. Different approaches exist for lossless image coding. We analyze the compression performance of three kinds of approaches, namely direct entropy, predictive and wavelet-based coding. The results from our theoretical model are compared to simulated results from standard algorithms that base on the three approaches. As long as no clipping occurs with increasing noise more bits are needed for lossless compression. We will show that for very noisy signals it is more advantageous to directly use an entropy coder without advanced preprocessing steps.

关键词： wavelet coding lossless image coding Line clipping Noisy image lossless compression lossless compression ratio Predictive

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A STUDY ON AN EVOLUTIONARY PIXEL PREDICTOR AND ITS PROPERTIES

A STUDY ON AN EVOLUTIONARY PIXEL PREDICTOR AND ITS PROPERTIE...

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16th IEEE International Conference on image Processing

作者： Takamura, Seishi Matsumura, Masaaki Yashima, Yoshiyuki NTT Corp NTT Cyber Space Labs Kanagawa 2390847 Japan

ISBN: (纸本)9781424456536

Evolutionary methods based on genetic programming (GP) enable dynamic algorithm generation, and have been successfully applied to many areas such as plant control, robot control, and stock market prediction. However, conventional image/video coding methods such as JPEG and H.264 all use fixed (non-dynamic) algorithms without exception. In this article, we introduce a GP-based image predictor that is specifically evolved for each input image. Experimental results demonstrate 2.9 % less entropy (overhead included) than CALIC's gradient adjusted predictor.

关键词： lossless image coding genetic algorithms prediction methods

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