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Efficient Variable Rate image Compression With Multi-Scale Decomposition Network

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第12期29卷 3687-3700页

作者： Cai, Chunlei Chen, Li Zhang, Xiaoyun Gao, Zhiyong Shanghai Jiao Tong Univ Dept Elect Engn Inst Image Commun & Network Engn Shanghai 200240 Peoples R China

While deep learning image compression methods have shown an impressive coding performance, most of them output a single-optimized-compression rate using a trained-specific network. However, in practice, it is essential to support the variable rate compression or meet a target rate with a high-coding performance. This paper proposes a novel image compression method, making it possible for a single convolutional neural network (CNN) model to generate the variable rate efficiently with an optimized rate-distortion (RD) performance. The method consists of CNN-based multi-scale decomposition transform and content adaptive rate allocation. Specifically, the transform network is learned to decompose the input image into several scales of representations while optimizing the RD performance for all scales. Rate allocation algorithms for two typical scenarios are provided to determine the optimal scale of each image block for a given target rate or quality factor. For a target rate, the allocation is adaptive based on content complexity. In addition, for a target quality factor which indicates a tradeoff between the rate and the quality, the optimal scale is determined by minimizing the RD cost. The experimental results have shown that our method has outperformed the JPEG2000 and BPG standards with high efficiency and the state-of-the-art RD performance as measured by the multi-scale structural similarity index metric. Moreover, our method can strictly control the rate to generate the target compression result.

关键词： image coding Resource management Transforms Transform coding Codecs Laplace equations Standards Lossy image compression multi-scale decomposition transform content adaptive rate allocation variable rate image compression convolutional neural network

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A novel low bit rate side match vector quantization algorithm based on structed state codebook

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第12期78卷 16965-16977页

作者： Wang, Yang Pan, Zhibin Li, Rui Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian Shaanxi Peoples R China

Side match vector quantization (SMVQ) is a widely used image compression algorithm for data hiding applications. Compared with conventional vector quantization (VQ) algorithm, a smaller and more powerful state codebook (SC) which is generated by utilizing the correlation in natural image is used in SMVQ to achieve low bit rate. However, the visual quality of reconstructed image by using SMVQ is significantly decreased. In this paper, a novel low bit rate coding algorithm named structured SMVQ (SSMVQ) is proposed. The size of SSMVQ's SC is flexible and the SC of SSMVQ is composed by a smaller SC of conventional SMVQ and a supporting codebook which is newly introduced in this paper. Experimental results show that the proposed structed SMVQ is able to achieve satisfactory PSNR when the bit rate is extremely low.

关键词： VQ SMVQ image coding Supporting codebook Structed codebook

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Gaussian channel transmission of images and audio files using cryptcoding

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IET COMMUNICATIONS 2019年第11期13卷 1625-1632页

作者： Bakeva, Verica Popovska-Mitrovikj, Aleksandra Mechkaroska, Daniela Dimitrova, Vesna Jakimovski, Boro Ilievski, Vladimir Ss Cyril & Methodius Univ Skopje Fac Comp Sci & Engn POB 393 Skopje North Macedonia Ecole Polytech Fed Lausanne Lausanne Switzerland

Random codes based on quasigroups (RCBQ) are cryptcodes, i.e. they are error-correcting codes, which provide information security. Cut-Decoding and 4-Sets-Cut-Decoding algorithms for these codes are defined elsewhere. Also, the performance of these codes for the transmission of text messages is investigated elsewhere. In this study, the authors investigate the RCBQ's performance with Cut-Decoding and 4-Sets-Cut-Decoding algorithms for transmission of images and audio files through a Gaussian channel. They compare experimental results for both coding/decoding algorithms and for different values of signal-to-noise ratio. In all experiments, the differences between the transmitted and decoded image or audio file are considered. Experimentally obtained values for bit-error rate and packet error rate and the decoding speed of both algorithms are compared. Also, two filters for enhancing the quality of the images decoded using RCBQ are proposed.

关键词： random codes decoding Gaussian channels error correction codes channel coding error statistics cryptography image coding audio coding 4-sets-cut-decoding algorithms audio files decoded image bit-error rate packet error rate decoding speed Gaussian channel transmission random codes error-correcting codes coding algorithm cryptcoding information security cut-decoding algorithm RCBQ performance quasigroups image files

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Matrix permutation meets block compressed sensing

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2019年 60卷 69-78页

作者： Zhang, Bo Liu, Yulin Zhuang, Jie Wang, Kai Cao, Yuqiang Army Engn Univ Commun NCO Acad Chongqing 400035 Peoples R China China Elect Syst Engn Corp Shenyang 110005 Liaoning Peoples R China Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Sichuan Peoples R China

Traditional block compressed sensing (CS) schemes encode nature images via a fixed sampling rate without taking the sparsity level differences among the blocks into consideration. In order to improve sampling efficiency, permutation-based block CS (BCS) schemes are proposed. In these schemes, the crux is to find a good permutation strategy which can make the nonzero entries evenly distributed among the blocks. In order to make the nonzero entries distributed among the blocks as evenly as possible, a novel matrix permutation strategy is proposed in this paper. Then, a BCS scheme with matrix permutation (BCS-MP) is proposed, which can be utilized to encode nature images effectively. Simulation results show that the proposed approach gets a significant gain of peak signal-to-noise ratio (PSNR) of the reconstructed-images compared with the state-of-the-art permutation-based ones and the traditional non-permutation one at the cost of slightly increasing the encoding time. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Matrix permutation image compression image coding Block compressed sensing

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Microshift: An Efficient image Compression Algorithm for Hardware

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2019年第11期29卷 3430-3443页

作者： Zhang, Bo Sander, Pedro V. Tsui, Chi-Ying Bermak, Amine Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Hamad Bin Khalifa Univ Informat & Comp Technol Div Doha Qatar

In this paper, we propose a lossy image compression algorithm called microshift. We employ an algorithm-hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our method, the image is first micro-shifted, and then the sub-quantized values are further compressed. Two methods, FAST and MRF models, are proposed to recover the bitdepth by exploiting the spatial correlation of natural images. Both methods can decompress images progressively. On an average, our compression algorithm can compress images to 1.25-bits per pixel with a resulting quality that outperforms the state-of-the-art on-chip compression algorithms in both peak signal-to-noise ratio and structual similarity. Then, we propose a hardware architecture and implement the algorithm on an FPGA. The results on the ASIC design further validate the low-hardware complexity and high-power efficiency, showing that our method is promising, particularly for low-power wireless vision sensor networks.

关键词： image coding Uncertainty Hardware Compression algorithms Quantization (signal) Complexity theory image quality Microshift MRF model on-chip image compression image sensor FPGA implementation

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Teaching Digital Signal Processing With a Challenge on image Forensics

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IEEE SIGNAL PROCESSING MAGAZINE 2019年第2期36卷 101-109页

作者： Pasquini, Cecilia Boato, Giulia Boehme, Rainer EURASIP Special Area Teams Biometr Data Forens & Secur London England Univ Trento Dept Informat Engn & Comp Sci Trento Italy Natl Interuniv Consortium Telecommun Parma Italy IEEE Multimedia Signal Proc Tech Comm Piscataway NJ USA IEEE Informat Forens & Se cur Tech Comm Piscataway NJ USA Univ Innsbruck Dept Comp Sci Secur & Privacy Innsbruck Austria Univ Munster Informat Syst Munster Germany

With the advent of ubiquitous sensing and real-time data processing, the demand for engineers with solid signal processing skills has exceeded the supply by a large margin. However, even students in technical subjects often perceive signal processing as demanding and somewhat dry [4]. In many curricula, signal processing must compete with more attractive classes, such as hands-on courses on programming, robotics, or computer graphics. Hence, new directions in signal processing education are needed [1], [9], [10].

关键词： Forensics image coding Transform coding Digital images Signal processing Real-time systems Forgery

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Objective estimation of subjective image quality assessment using multi-parameter prediction

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IET image PROCESSING 2019年第13期13卷 2428-2435页

作者： Maksimovic-Moicevic, Sanja Lukac, Zeljko Temerinac, Miodrag Univ Novi Sad Fac Tech Sci Dositeja Obradovica Sq 6 Novi Sad Serbia

Objective evaluation of a subjective image quality assessment plays a significant role in the various image processing applications, such as compression, interpolation and noise reduction. The subjective image quality assessment does not only depend on some objective measurable artefacts, but also on image content and kind of distortions. Thus, a multi-parameter prediction of the objective image quality assessment is proposed in this study. The prediction parameters are found minimising the mean square error related to the known subjective image quality measure (DMOS). This approach includes mostly used image quality metrics (peak signal-to-noise ratio, multi-scale structural similarity image measure, feature similarity image measure, video quality measure) and two-dimensional image quality metrics (2D IQM). The proposed multi-parameter prediction has been verified on the test image database (LIVE) for compression, noise and blur distortions with available subjective image quality measures (DMOS). More reliable estimations are obtained using multi-parameter prediction instead of only one measure. The best results are reached when an image content indicator is combined with the 2D IQM measure separately for different kinds of distortions.

关键词： feature extraction image fusion image coding image processing data compression mean square error methods visual databases distortion subjective image quality assessment multiparameter prediction image processing applications objective measurable artefacts objective image quality assessment known subjective image quality measure multiscale structural similarity image measure feature similarity image measure video quality measure two-dimensional image quality metrics test image database available subjective image quality measures image content indicator

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Memorability-based image compression

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IET image PROCESSING 2019年第9期13卷 1490-1501页

作者： Khanna, Meera Thapar Ralekar, Chetan Goel, Anurika Chaudhury, Santanu Lall, Brejesh Indian Inst Technol Delhi Dept Elect Engn Multimedia Lab New Delhi 110016 India Goldman Sachs Bengaluru Bengaluru Karnataka India CSIR Cent Elect Engn Res Inst Pilani Rajasthan India

This study is concerned with achieving the image compression using the concept of memorability. The authors have used memorability of an image, as a perceptual measure while image coding. In the proposed approach, a region-of-interest-based memorability preserving image compression algorithm which is accomplished via two sub-processes namely, memorability prediction and image compression is introduced. The memorability of images is predicted using convolutional neural network and restricted Boltzmann machine features. Based on these features, the memorability score of individual patches in an image is calculated and these scores are used to generate the memorability map. These memorability map values are used for optimised image compression. In order to validate the results, an eye tracking experiment with human participants is performed. The comparative analysis shows that the memorability-based compression outperforms the state-of-the-art compression techniques.

关键词： Boltzmann machines data compression image coding convolutional neural nets region-of-interest-based memorability preserving image compression algorithm memorability prediction memorability score memorability map values optimised image compression image coding convolutional neural network restricted Boltzmann machine features eye tracking experiment human participants

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Compressed Imaging to Reduce Storage in Adjoint-State Calculations

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第11期57卷 9236-9241页

作者： Zand, Toktam Malcolm, Alison Gholami, Ali Richardson, Alan Univ Tehran Inst Geophys Tehran *** Iran Univ Newfoundland Dept Earth Sci St John NF A1C 5S7 Canada Ausar Geophys Dalkey A96 F242 Ireland

To generate seismic images of the subsurface with adjoint-state methods such as reverse-time migration (RTM) and full-waveform inversion (FWI), the gradient of a misfit function is computed efficiently by applying what is referred to as an imaging condition to the forward propagated source wavefield and the backward propagated adjoint wavefield. In order to reduce the storage in adjoint-state calculations, we evaluate the imaging condition only at a randomly selected subset of spatial grid points (compressed imaging) and then efficiently reconstruct the full image from the imaged points via compressed sensing theory, which combines the compressibility characteristics of seismic images and convex optimization tools for the reconstruction. We use the second-order total variation regularization for the reconstruction and, using different numerical tests from RTM and FWI, we show that the new method allows a significant reduction in wavefield storage, while still recovering the full image accurately. Furthermore, regularization, applied on the gradient during the reconstruction stage improves the convergence of the FWI algorithm.

关键词： Imaging image reconstruction image coding Optimization Memory management Checkpointing Mathematical model Adjoint-state method compressed sensing (CS) full-waveform inversion reverse-time migration total variation

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Accurate full-resolution reconstruction of spike-encoded image time series using random matrix theory

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ELECTRONICS LETTERS 2019年第4期55卷 182-183页

作者： Chen, Rui Yang, Changshui Jia, Huizhu Huang, Tiejun Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China Peking Univ Inst Digital Media Beijing 100871 Peoples R China

A new application-specific approach is proposed to reconstruct the full-resolution grey-scale image from its binary spike series. The noisy observation of each pixel value is first computed by counting the spike pulses of a given event duration. The relationship between the light intensity and the signal-dependent noise is utilised in a linear image formation model to compensate truncated intensity error. Then, the minimum variance estimator for the true intensities is derived from this effective model. This estimation problem is non-iteratively solved by applying the asymptotic results of random matrix theory. Experimental results have demonstrated that the algorithm can reliably infer true pixel intensities and is superior to the simple spike counting in terms of both estimation accuracy and robustness.

关键词： image coding image denoising image reconstruction image resolution matrix algebra time series

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