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Medical JPEG image steganography method according to the distortion reduction criterion based on an imperialist competitive algorithm

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IET image PROCESSING 2021年第3期15卷 705-714页

作者： Damghani, Hamidreza Mofrad, Farshid Babapour Damghani, Leila Islamic Azad Univ Dept Elect & Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Med Radiat Engn Sci & Res Branch Tehran Iran Kharazmi Univ Dept Comp Sci Tehran Iran

In most of the digital steganography methods provided for natural digital images, the embedding of the confidential message is based on the minimisation of the defined distortion functions. It is often done based on choosing the most optimal criterion of distortion. Although the distortion functions are designed innovatively, steganography algorithms will be optimal. In such approaches, embedding interactions are often overlooked. Unlike usual images that have areas with a variety of tissue features, there are many smooth areas in medical images that will make the changes more visible if they are manipulated. Therefore, this study presents an adaptive approach that comes from the interactions between the changes made during the embedding algorithm to reduce the probability of recognising the message embedded in medical images and reducing the distortion caused by embedding in a discrete cosine transform space and based on the imperialist competitive algorithm for joint photographic experts group images, especially in medical images due to the importance of information steganography in them. The results obtained show the high efficiency of the proposed algorithm in comparison with the state-of-the-art methods that are presented in this area.

关键词： Integral transforms Other topics in statistics Cryptography Optical, image and video signal processing image and video coding Computer vision and image processing techniques Data security

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Hardware-software co-design architecture for Joint Photo Expert Graphic XR encoder

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IET image PROCESSING 2012年第9期6卷 1284-1292页

作者： Tseng, C. -F. Lai, Y. -T. Natl Cheng Kung Univ Dept Elect Engn Tainan 701 Taiwan

In recent years, because of to the fast growing of internet and digital multi-media, new image compression algorithms are needed to reduce the transferring bandwidth and the decay of image quality. Joint Photo Expert Graphic (JPEG) extended range (XR) is a new standard for still image compression. Compared to previous standards, it provides better image quality and plentiful pixel format. However, the better image quality often comes with the higher computation complexity. In this study, the authors propose a hardware-software co-design architecture for JPEG XR encoder. For photo overlap transform (POT), and photo core transform (PCT), the complicated parts of JPEG XR, the authors propose new algorithms which eliminate data dependency in the basic filters of POT and PCT so that operations can be executed in parallel. The processing rate of the new algorithms is much faster. The authors also demonstrate a basic filter with multiple functions, which can be time-shared by POT and PCT;the hardware cost can be therefore reduced. Moreover, a memory saving scheme is proposed. Therefore the proposed architecture is suitable for stand-alone application-specific integrated circuit (ASIC). In our co-design, POT and PCT are implemented in hardware and then mapped to field programmable gate array (FPGA), whereas the other procedures of JPEG XR such as quantisation are executed by ARM926-EJ processor. The overall performance of our hardware architecture is 50 fps for 4:4:4 HDTV format at 166 MHz.

关键词： frequency 166 MHz image quality ASIC Filtering methods in signal processing photo core transform ARM926-EJ processor data compression hardware-software codesign digital multimedia image compression algorithm JPEG XR encoder field programmable gate arrays Hardware-software codesign Internet Electronic engineering computing FPGA joint photo expert graphic XR encoder Computer vision and image processing techniques computer architecture image and video coding still image compression application specific integrated circuits image coding hardware architecture photo overlap transform data dependency Integral transforms POT quantisation (signal) Logic circuits Computer architecture computation complexity quantisation JPEG extended range stand-alone application-specific integrated circuit PCT Logic and switching circuits memory saving scheme HDTV format filtering theory filter bandwidth transfer hardware-software co-design architecture field programmable gate array Digital circuit design, modelling and testing transforms

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Fast intra-prediction with near pixel correlation approach for H.264/AVC system

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IET image PROCESSING 2008年第4期2卷 185-193页

作者： Hsia, S. -C. Chou, Y. -C. Natl Kaohsiung First Univ Sci & Technol Dept Elect Engn Kaohsiung 824 Taiwan Natl Kaohsiung First Univ Sci & Technol Dept Comp & Commun Engn Kaohsiung 824 Taiwan

A fast intra-prediction algorithm to determine the codingmode for 4 x 4 blocks is presented. The near pixel-correlation integration approach is proposed to compute the parameter for each mode along the predictive direction. The parameters that can be easily estimated with the simple computation are useful to represent the feature of block mode. On the basis of the fast algorithm, only three to four candidate modes are selected from nine reference blocks to find the best coding mode with a minimum rate-distortion (RD) cost for H. 264 system coding. The complexity of intra-prediction can be reduced similar to 60% compared with the exhaustive search and the processing time of full H. 264 coding can be reduced similar to 45% while maintaining the same video quality, but the bit-rate increases similar to 2%. When compared with the recent competing algorithms, the proposed algorithm can achieve better coding efficiency with faster speed, higher quality, lower bit-rate and lower matching error in the experimental results.

关键词： block codes image resolution correlation methods predictive direction video coding near pixel-correlation integration approach exhaustive search image and video coding rate distortion theory fast intraprediction algorithms error matching minimum rate-distortion cost H.264-AVC system video signal processing

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Compression artifact reduction of low bit-rate videos via deep neural networks using self-similarity prior

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IET image PROCESSING 2023年第2期17卷 480-491页

作者： Liu, Dongsheng Yu, Runyi Chen, Wei-Gang Zhejiang Gongshang Univ Sch Comp & Informat Engn Hangzhou 0011 Peoples R China Eastern Mediterranean Univ Dept Elect & Elect Engn Gazimagusa Turkey

This paper presents a hybrid scheme that integratedly uses self-similarity prior and deep convolutional neural network (CNN) fusion for compression artifact reduction in low bit-rate video applications. Based on the temporal correlation hypothesis, the self-similarity prior is extended to the temporal domain by using as references not only the current decoded frame but also its neighbouring frames. Furthermore, being cognizant of that the bicubic downsampling process can typically improve the perceptual quality of a video coded at low bit-rate, for each small patch in the current frame, we search for similar patches in down-scaled versions of these references, and then form several self-similarity prior based predictions by tiling these similar patches at corresponding positions. To further exploit information flow across scales, a deep CNN model is constructed that contains two sub-networks to estimate the final output. One sub-network takes the self-similarity prior based predictions along with the decoded frame itself;and the other takes the down-scaled versions of these frames as network input. Experimental results demonstrate that the proposed method can remarkably improve, both subjectively and objectively, quality of compressed video sequences of low bit-rates.

关键词： low bit-rate video applications video signal processing neighbouring frames image and video coding Neural nets video coding deep neural networks Optical, image and video signal processing Computer vision and image processing techniques convolutional neural nets current decoded frame self-similarity prior based predictions deep learning (artificial intelligence) deep CNN model data compression image sequences video signal processing temporal domain low bit-rate videos image resolution network input compressed video sequences temporal correlation hypothesis compression artifact reduction

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Robust watermarking using fractional wavelet packet transform

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IET image PROCESSING 2012年第4期6卷 386-397页

作者： Bhatnagar, G. Raman, B. Wu, Q. M. J. Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Indian Inst Technol Dept Math Roorkee Uttar Pradesh India

In this study, the concept of fractional wavelet packet transform is explored with its application in digital watermarking. The core idea of the proposed watermarking scheme is to decompose an image via fractional wavelet packet transform and then a reference image is created by changing the positions of all frequency sub-bands at each level with respect to some rule which is secret and only known to the owner/creator. For embedding, the reference image is segmented into non-overlapping blocks and modify its singular values with the watermark singular values. Finally, a reliable watermark extraction algorithm is developed for the extraction of watermark from the distorted image. The feasibility of this method and its robustness against different kind of attacks are verified by computer simulations.

关键词： Computer vision and image processing techniques image watermarking image and video coding fractional wavelet packet transform digital watermarking Data security wavelet transforms watermark extraction algorithm Integral transforms image segmentation

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Congestion reduction in transmission of video over ABR networks

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IEE PROCEEDINGS-COMMUNICATIONS 2000年第6期147卷 305-310页

作者： Rosdiana, E Azmoodeh, H Ghanbari, M Univ Essex Dept Elect Syst Engn Colchester CO4 3SQ Essex England BT Adv Commun Ctr Data Network Engn Adv Commun Technol Ctr Ipswich IP5 ERE Suffolk England

In transmission of bursty traffic such as video and data, a network with a fu;ed capacity may have some unused bandwidth. The available bit rate (ABR) scheme in ATM networks can make use of this spare channel capacity as a lower priority service. ABR has been developed initially for delay-tolerant services such as data. The authors demonstrate how, with the help of a video transcoder, delay-sensitive services such as video may be transmitted over ABR. The transcoder further compresses the video bit stream according to the rates dictated by the network with minimum degradation to the picture quality. The video quality has been optimised by combining the classical minimum cell rate (MCR) with the subjectively acceptable minimum quality (SAMQ). The performance of two different coding formats for ABR sources, namely the constant bit rate (CBR) and variable bit rate (VBR) video input to the transcoder are compared. The authors also present techniques for further reduction of congestion at the multiplexing switches and report on the impact of the forward and backward link delays on the ABR performance with a video transcoder.

关键词： ABR networks variable rate codes video transmission video coding spare channel capacity telecommunication congestion control delay-tolerant services degradation video bit stream multiplexing switches video transcoder delays variable bit rate ATM networks Communication switching available bit rate picture quality lower priority service channel capacity delay-sensitive services video quality classical minimum cell rate backward link delay forward link delay subjectively acceptable minimum quality image and video coding coding formats asynchronous transfer mode bursty traffic constant bit rate congestion reduction

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Advanced fine-tuning procedures to enhance DNN robustness in visual coding for machines

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EURASIP JOURNAL ON image AND video PROCESSING 2024年第1期2024卷 31页

作者： Marie, Alban Desnos, Karol Mercat, Alexandre Morin, Luce Vanne, Jarno Zhang, Lu Tampere Univ Ultra Video Grp Tampere Finland Univ Rennes INSA Rennes CNRS IETRUMR 6164 Rennes France

video coding for Machines (VCM) is gaining momentum in applications like autonomous driving, industry manufacturing, and surveillance, where the robustness of machine learning algorithms against coding artifacts is one of the key success factors. This work complements the MPEG/JVET standardization efforts in improving the resilience of deep neural network (DNN)-based machine models against such coding artifacts by proposing the following three advanced fine-tuning procedures for their training: (1) the progressive increase of the distortion strength as the training proceeds;(2) the incorporation of a regularization term in the original loss function to minimize the distance between predictions on compressed and original content;and (3) a joint training procedure that combines the proposed two approaches. These proposals were evaluated against a conventional fine-tuning anchor on two different machine tasks and datasets: image classification on imageNet and semantic segmentation on Cityscapes. Our joint training procedure is shown to reduce the training time in both cases and still obtain a 2.4% coding gain in image classification and 7.4% in semantic segmentation, whereas a slight increase in training time can bring up to 9.4% better coding efficiency for the segmentation. All these coding gains are obtained without any additional inference or encoding time. As these advanced fine-tuning procedures are standard-compliant, they offer the potential to have a significant impact on visual coding for machine applications.

关键词： video coding for Machines (VCM) coding artifacts image and video coding Deep neural network (DNN) Deep learning Fine-tuning

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Maximum likelihood approach to image texture and acoustic signal classification

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IEE PROCEEDINGS-VISION image AND SIGNAL PROCESSING 1999年第1期146卷 34-39页

作者： Thyagarajan, KS Nguyen, T Persons, CE San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA RDTE NCCOSC San Diego CA 92152 USA

The authors describe a method of classifying natural textures based on the maximum likelihood parameter estimation technique. The novelty of the technique lies in the use of textural features that are derived from the subbands of a wavelet transformed image via the co-occurrence matrices. A maximum likelihood classifier is designed using a set of training texture samples. Ten different Brodotz textures have been classified using this procedure with an average classification accuracy of 99.7%. The main emphasis is to apply this technique to the classification of underwater acoustic signals. A time-frequency plot is obtained for each segment of the acoustic signal and then converted to an intensity pattern. The textural classification scheme is then applied to the intensity patterns of the acoustic signals. Eight different underwater acoustic signals have been classified by this procedure with an average accuracy of 99.99%.

关键词： Sonar and acoustic radar image classification Acoustic signal processing Simulation, modelling and identification maximum likelihood estimation transform coding acoustic signal processing average accuracy time-frequency plot intensity patterns maximum likelihood parameter estimation co-occurrence matrices training texture samples image representation image resolution Computer vision and image processing techniques Brodotz textures multiresolution representation acoustic signal classification image coding underwater sound average classification accuracy subbands Integral transforms in numerical analysis Underwater sound textural features image texture image and video coding wavelet transformed image underwater acoustic signals image texture classification wavelet transforms maximum likelihood classifier wavelet transform

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New image quality metric using random projection

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IET image PROCESSING 2012年第9期6卷 1246-1255页

作者： Kim, D. -O Park, R. -H. Sogang Univ Dept Elect Engn Seoul 121742 South Korea

This study proposes a new image quality metric using random projection (RP). Generally, distortion is wide spread over the entire region in an image when the image is degraded by compression, blurring, additive noise and so on, which signifies that it is not necessary to compute the degree of distortion at every pixel. RP has been used for representing a signal to a small number of measurements. In this study, to evaluate image quality, distortions between the reference and distorted images are measured at a small number of pixels that are preselected or randomly chosen through the RP and to beat the simple random sampling, the saliency-based weighted sum is used. Experimental results with the laboratory for image and video engineering data set show the effectiveness of the proposed quality metric.

关键词： additive noise video engineering data set blurring Computer vision and image processing techniques reference image random projection distorted image image and video coding image coding image compression image quality metric saliency-based weighted sum data compression

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Non-intrusive load monitoring by voltage-current trajectory enabled asymmetric deep supervised hashing

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IET GENERATION TRANSMISSION & DISTRIBUTION 2021年第21期15卷 3066-3080页

作者： Han, Yinghua Xu, Yao Huo, Yaxin Zhao, Qiang Northeastern Univ Qinhuangdao Sch Comp & Commun Engn 143 Taishan Rd Qinhuangdao Hebei Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

With the development of smart grids, appliance-level data information plays a vital role in smart power consumption. Nowadays, appliance signatures detected by non-intrusive load monitoring (NILM) can be used for anomaly detection, demand response, and electricity management. These applications increase the requirements for the accuracy of appliance identification in NILM. And it has been proved that the voltage-current (V-I) trajectory can be applied as an effective load signature to represent the electrical characteristics of appliances with different statuses in previous researches. In this paper, a V-I trajectory enabled asymmetric deep supervised hashing (ADSH) method has been proposed for NILM. ADSH method converts load identification into the large-scale approximate nearest neighbour search. Different from the existing methods, ADSH treats the query images and database images in an asymmetric way in order to improve accuracy. More specifically, ADSH learns a deep hash function only for query images, while the hash codes for database images are directly learned. The experimental results on REDD and PLAID datasets show that the proposed method significantly improves the accuracy of load identification compared with state-of-art methods.

关键词： Other topics in statistics Optimisation techniques image and video coding Power system management, operation and economics Domestic appliances Other topics in statistics Computer vision and image processing techniques Information retrieval techniques Power engineering computing Supervised learning

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