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Motion-compensated orthogonal transforms for multiview video coding

Motion-compensated orthogonal transforms for multiview video...

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European signal processing conference (EUSIPCO)

作者： Markus Flierl Max Planck Center for Visual Computing and Communication Stanford University California

This paper discusses coding of multiview video with motion-compensated orthogonal transforms. These transforms have recently been introduced to generate strictly orthonormal decompositions of image sequences. Orthonormality is maintained for arbitrary motion compensation among the images. This is of particular interest for coding applications as motion compensation is inaccurate due to quantization of motion information. This is in contrast to the well known motion-compensated lifted wavelets where the properties of the decomposition are motion-dependent. This disadvantage affects compression efficiency and in particular that of multiview video signals. Multiview video coding schemes utilize motion and disparity compensation when decomposing the multiview imagery into view-temporal subbands. Usually decompositions in time and view direction are cascaded. Motion-compensated lifted wavelets suffer from their motion-dependent decomposition and in particular if cascaded for multiview video coding. On the other hand motion and disparity compensated orthogonal transforms offer the advantage that the overall decomposition is still strictly orthonormal. This paper investigates the advantages of strict orthonormality that is offered by motion-compensated orthogonal transforms.

关键词： Video coding Encoding Motion compensation wavelet transforms Radiation detectors image sequences

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Multiresolutional encoding and decoding in embedded image and video coders

Multiresolutional encoding and decoding in embedded image an...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zixiang Xiong Beong-Jo Kim W.A. Pearlman Department of Electrical Engineering University of Hawai Honolulu HI USA Center for Image Processing Research Rensselaer Polytechnic Institute Troy NY USA

We address multiresolutional encoding and decoding within the embedded zerotree wavelet (EZW) framework for both images and video. By varying a resolution parameter, one can obtain decoded images at different resolutions from one single encoded bitstream, which is already rate scalable for EZW coders. Similarly one can decode video sequences at different rates and different spatial and temporal resolutions from one bitstream. Furthermore, a layered bitstream can be generated with multiresolutional encoding, from which the higher resolution layers can be used to increase the spatial/temporal resolution of the images/video obtained from the low resolution layer. In other words, we have achieved full scalability in rate and partial scalability in space and time. This added spatial/temporal scalability is significant for emerging multimedia applications such as fast decoding, image/video database browsing, telemedicine, multipoint video conferencing, and distance learning.

关键词： image resolution image coding Decoding Spatial resolution Videoconference Scalability Multimedia databases Video sequences image databases Spatial databases

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Optimizing Hybrid Sparse and Block-Based Compressed Sensing: A Performance Study for Biomedical Imaging and Video Surveillance applications

Optimizing Hybrid Sparse and Block-Based Compressed Sensing:...

引用

Innovations in High Speed Communication and signal processing (IHCSP), International conference on

作者： Nitish Vashisht M. Sudhakara Reddy Gajanan H Chavhan P. K. Miniappan Praney Madan Venkatasubramanian D Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Sciences Physics & Electronics JAIN (Deemed to be University) Bangalore Karnataka E&TC Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

One of the most promising fields, compressed sensing has found a home in video streaming applications, where it seeks to compress or sparsely represent signals by reducing their dimensions. The sparse representation is a good approximation to the signal since the amplitude of the coefficient’s decays according to the power law. signals are projected into low-dimensional space to generate measurement vectors. A sparse signal may be accurately rebuilt using the measurement vector. signals or noise that are not sparse but can be compressed and might result in an approximation. images in the frequency or wavelet domain are ideal for Compressed Sensing because they are compressible by nature. A more precise reconstruction of the original picture is possible due to an improvement in reconstructed image quality and a reduction in the compression ratio, which is the ratio of the dimensions of the number of pixels to the measurement *** Sparse and Block-based Compressed Sensing Algorithm (HSBCA) is the name of the approach that merged sparse-based and block-based compressed sensing algorithms. Block Compressed Sensing, the first module of HSSBCA, processes each message by dividing it into numerous picture regions, which results in decreased run time. In contrast, performance drops while picture reconstruction is underway; to remedy this, the research employs the second module, the sparse compressed sensing method, to boost reconstruction efficiency.

关键词： image coding wavelet domain Sparse approximation Magnetic resonance imaging signal processing algorithms Biomedical measurement Streaming media Vectors image reconstruction Compressed sensing

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Efficient medical image transformation method for lossless compression by considering real time applications

Efficient medical image transformation method for lossless c...

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International conference on signal processing and Communication Systems, ICSPCS

作者： Farshid Sepehrband Mohammad Mortazavi Seyed Ghorshi Jeiran Choupan School of Science and Engineering Sharif University of Technology Iran

Medical images contain human body pictures and used widely in diagnosis and surgical purposes. Compression is needed for medical images for some applications such as profiling patient's data or transmission systems Due to the importance of the information of medical images, lossless or visually lossless compression preferred. Lossless compression mainly consists of transformation and encoding steps. On the other hand, hardware implementation of lossless compression algorithm accelerates real time tasks such as online diagnosis and telemedicine. Lossless JPEG, JPEG-LS and lossless version of JPEG2000 are few well known methods for lossless compression. This paper is focused on the transformation step of compression and introduced a new transformation which is efficient in both entropy reduction and computational complexity. A new method is then achieved by improving the perdition model which is used in lossless JPEG. Our new transformation increases the energy compaction of prediction model and as a result reduces entropy value of transformed image. However, our new method is low complex. After a mathematical proof for efficiency of the new method, it is applied to more than hundreds of test-cases and the results are compared with previous methods and it shows about 8 percent improvement in average. As a result, the new algorithm shows a better efficiency for transforming lossless medical images, especially for online applications.

关键词： image coding Transform coding Entropy Compaction Biomedical imaging Pixel Discrete wavelet transforms

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A HIGH-PERFORMANCE ARCHITECTURE OF JPEG2000 ENCODER

A HIGH-PERFORMANCE ARCHITECTURE OF JPEG2000 ENCODER

引用

European signal processing conference

作者： Damian Modrzyk Michal Staworko Institute of Telecommunications Warsaw University of Technology

This article presents hardware architecture of JPEG2000 encoder core, oriented for HD video broadcast and surveillance applications. Thanks to developed efficient 2-D DWT engine that is capable of computing four coefficients per clock cycle, and adopted two EBCOT TIER-1 modules, with smart switching of the channels, the maximum compression speed of 180 Msamples/s at 100 MHz, in lossy mode is achieved. The architecture is implemented in VHDL and synthesised for FPGA devices, and ASIC 0.13 μm CMOS technology. Performance simulations, conducted on a set of natural images and video sequences, have revealed that the encoder is capable of processing 1080p 4:4:4 signal with a speed of 30 frames per second. Additionally, an excellent quality of reconstructed images has been observed, with respect to the reference, software encoder.

关键词： Transform coding Discrete wavelet transforms image coding Encoding Quantization (signal) Hardware

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Quasi-analytic directional wavelet packets: applications to image processing

Quasi-analytic directional wavelet packets: applications to ...

引用

International conference on Sampling Theory and applications (SampTA)

作者： Amir Z. Averbuch Valery A. Zheludev Sch. of Computer Science Tel Aviv University Tel Aviv Israel

Recently, a versatile library of quasi-analytic complex-valued wavelet packets (WPs) which originate from splines of arbitrary orders, was designed [1]. The real parts of the quasi-analytic WPs (qWPs) are the regular spline-based orthonormal WPs. The imaginary parts, which are slightly modified Hilbert transforms of the real parts, are the so-called complementary orthonormal WPs, which, unlike the symmetric regular WPs, are antisymmetric. Both regular and complementary WPs are well localized in time domain and their DFT spectra provide a variety of refined splits of the frequency domain. The waveforms can have arbitrary number of vanishing *** products of 1D quasi-analytic WPs (qWPs) provide a diversity of 2D waveforms oriented in multiple directions. The designed computational scheme enables us to get fast and easy implementation of the qWP transforms. The shapes of real and imaginary parts of the qWPs can be regarded as directional cosine waves with different frequencies modulated by localized low-frequency signals. For example, the set of the fourth-level WPs comprises waveforms which are oriented in 314 different directions and are oscillating with 256 different frequencies. Various combinations of qWPs form multiple frames in the 2D signal *** combination of the exceptional properties of the designed qWPs, such as unlimited directionality and oscillating structure of the waveforms, vanishing moments and refined frequency resolution, make them a powerful tool for image processing applications. The algorithms based on the qWPs proved to be competitive with the best existing methods in solving such classical image processing problems as denoising, inpainting and deblurring. The qWP algorithms are especially efficient for capturing edges and fine texture and oscillating patterns even in severely degraded *** to the above properties and next to unlimited diversity of testing waveforms, the qWPs have strong capabilities for extraction characte

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Comparative analysis on image compression techniques for chromosome images

Comparative analysis on image compression techniques for chr...

引用

International conference on Advances in Biomedical Engineering (ICABME)

作者： M. Mansour H. Mouhadjer A. Alipacha K. Draoui Faculty of Electrical Engineering University USTO-MB

image compression has emerged as a major research area due to the phenomenal growth of applications that generate process and transmit images. image compression can be sequential or progressive. Natural images contain edges, geometry, texture and other discontinuities (details that are oriented in various directions). In this paper, a comparative analysis of four images compression techniques (JPEG, wavelets, Bandelets and Ridgelets) applied to images of chromosomes is proposed. The interest of this study is to measure the sensitivity to the noise of these techniques when dealing with contour and texture of different forms of objects in the image. This synthesis and from results, proves that the studied factors (compression ratio, processing duration, coding error and signal noise ratio) have a characteristic influence on each other. This indicates that the choice of the result converges to an optimal compromise. Promising results are obtained.

关键词： image coding wavelet transforms Transform coding wavelet analysis Discrete cosine transforms Noise

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BAYESIAN ESTIMATION FOR THE MULTIFRACTALITY PARAMETER

BAYESIAN ESTIMATION FOR THE MULTIFRACTALITY PARAMETER

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IEEE International conference on Acoustics, Speech and signal processing

作者： Herwig Wendt Nicolas Dobigeon Jean-Yves Tourneret Patrice Abry University of Toulouse UMR CNRS 5505 INP-ENSEEIHT F-31062 Toulouse France ENS Lyon Physics Dept. UMR CNRS 5672 F-69007 Lyon France

ISBN: (纸本)9781479903573

Multifractal analysis has matured into a widely used signal and image processing tool. Due to the statistical nature of multifractal processes (strongly non-Gaussian and intricate dependence) the accurate estimation of multifractal parameters is very challenging in situations where the sample size is small (notably including a range of biomedical applications) and currently available estimators need to be improved. To overcome such limitations, the present contribution proposes a Bayesian estimation procedure for the multifraetality (or intermittence) parameter. Its originality is threefold: First, the use of wavelet leaders, a recently introduced multiresolution quantity that has been shown to yield significant benefits for multifractal analysis;Second, the construction of a simple yet generic semi-parametric model for the marginals and covariance structure of wavelet leaders for the large class of multiplicative cascade based multifractal processes;Third, the construction of original Bayesian estimators associated with the model and the constraints imposed by multifractal theory. Performance are numerically assessed and illustrated for synthetic multifractal processes for a range of multifractal parameter values. The proposed procedure yields significantly improved estimation performance for small sample sizes.

关键词： multifractal analysis Bayesian estimation wavelet leaders multiplicative cascade processes log-cumulants Bayes methods MULTIFRACTAL ANALYSIS Sample Size wavelets assessment procedure Creativity Leaders

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image Super Resolution Using Fractional Discrete wavelet Transformand Fractional Fast Fourier Transform

Image Super Resolution Using Fractional Discrete Wavelet Tra...

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International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Anita Ray Choudhury Vijay R. Dahake Department of Electronics and Telecommunication RamaraoAdik Institute of Tehnology Nerul Navi Mumbai Maharashtra

ISBN: (纸本)9781538614433;9781538614426

Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images. In various image processing fields super resolution is playing a very crucial part. The single-frame as well as multi-frame both the methods in SR are widely used in multiple day to day applications and systems from last few years. In this work, we are proposing an image resolution enhancement technique which has its basis on interpolation of the high-frequency sub-band images obtained by fractional discrete wavelet transform (FDWT). HereFDWT is used to decompose an input low-resolution image into different subbands. Then, interpolation between the high-frequency subband images and the input image takes place, followed by combining all these images to generate a new high-resolution image by using inverse FDWT. Directional selectivity which is provided by FDWT actually produces resolution enhancement, where the high-frequency subbands in different directions contribute to the sharpness of the high-frequency details such as edges. The quantitative peak signal-to-noise ratio (PSNR) and visual results show the superiority of the proposed technique over the conventional bicubic interpolation image resolution enhancement techniques.

关键词： Discrete wavelet transforms Interpolation signal resolution Spatial resolution

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Colour Volumetric Compression for Realistic View Synthesis applications

Colour Volumetric Compression for Realistic View Synthesis A...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： N. Anantrasirichai C. Nishan Canagarajah David W. Redmill David R. Bull Department of Electrical & Electronic Engineering University of Bristol Bristol UK

The colour volumetric data which is constructed from a set of multi-view images is capable of providing realistic immersive experience. However it is not widely applicable due to its manifold increase in bandwidth. This paper presents a novel framework to achieve scalable volumetric compression. Based on wavelet transformation, data rearrangement algorithm is proposed to compact volumetric data leading to high efficiency of transformation. The colour data is also rearranged by using the characteristics of human eye sensitivity. Moreover, the pre-processing for adaptive resolution is proposed in this paper. The low resolution overcomes the limitation of the data transmission at low bit rate, whilst the fine resolution improves the synthesised images' quality. The results of our proposed schemes show significant improvement of the compression performance over the traditional 3D coding.

关键词： image coding Bit rate Bandwidth Humans image resolution Data communication Medical services Biomedical equipment Data engineering Manifolds

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