Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.

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Image quality assessment: from error visibility to structural similarity

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern recognition procedure: the mean shift. For discrete data, we prove the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density. The relation of the mean shift procedure to the Nadaraya-Watson estimator from kernel regression and the robust M-estimators; of location is also established. Algorithms for two low-level vision tasks discontinuity-preserving smoothing and image segmentation - are described as applications. In these algorithms, the only user-set parameter is the resolution of the analysis, and either gray-level or color images are accepted as input. Extensive experimental results illustrate their excellent performance.

Mean shift: a robust approach toward feature space analysis

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Multiresolution image decomposition based on nonlinear filtering has received a lot of attention recently. In this research, we investigate the coding issue for one class of nonlinear multiresolution image decomposition based on mathematical morphology. We consider the use of opening and closing operations with a flat structure element to achieve image decomposition. The entropy and histogram of the difference images in the image pyramid are then examined. We give a numerical example to demonstrate potential advantages of the morphological filtering approach over the conventional linear filtering approach in the context of image coding. However, we also point out difficulties encountered in our study that have to be overcome before the method can be practically used.

Image compression using multiresolution morphological decomposition

A first-order-residual/second-order-residual (FOR/SOR) based video coding algorithm that incorporates the super macroblock (SMB) and the inter-frame stripe prediction (ISP) technique is proposed for high definition (HD) video coding in this work. We first examine the limitation of the SMB in high-bit-rate coding, and show that a simple extension of the block-size is not sufficient to get a significant improvement in coding performance. Then, we introduce the FOR/SOR coding technique to resolve this issue. For the FOR coding, we apply a larger quantization stepize and motion-predictive coding to remove the temporal correlation. Then, for the SOR coding, we propose an efficient prediction method, called the inter-frame stripe prediction (ISP) technique, to remove the structured residuals after the FOR coding to achieve a higher coding gain. It is demonstrated by experimental results that the proposed FOR/SOR algorithm outperforms H.264/AVC by a significant margin; namely, about 21.57% in the bit rate saving or 1.09dB in the PSNR improvement.

FOR/SOR video coding with super macroblock and inter-frame stripe prediction

Algorithms of classifying and segmenting bit streams with different source content (such as speech, text and image, etc.) and different coding methods (such as ADPCM, (mu) -law, tiff, gif and JPEG, etc.) in a communication channel are investigated. In previous work, we focused on the separation of fixed- and variable-length coded bit streams, and the classification of two variable-length coded bit streams by using Fourier analysis and entropy feature. In this work, we consider the classification of multiple (more than two sources) compressed bit streams by using vector quantization (VQ) and Gaussian mixture modeling (GMM). The performance of the VQ and GMM techniques depend on various parameters such as the size of the codebook, the number of mixtures and the test segment length. It is demonstrated with experiments that both VQ and GMM outperform the single entropy feature. It is also shown that GMM generally outperforms VQ.

Compressed bit stream classification using VQ and GMM

Diffusion MRI (DMRI) signal is characterized by the self-diffusion propagation profile within the brain white matters. Despite previous efforts on quantification of this physical phenomenon in the literature, most existing methods suffer from a number of constraints which severely limit the extent of their practical applicability. In this work, we relax these limitations to a large degree by addressing the solution of the self-diffusion process in its most general partial differential equation (PDE) form. Specifically, we develop an approach based on the finite element method (FEM) to obtain numerical self-diffusion solution in multi-compartments models of the white matters. Besides, due to sensitivity of the DMRI signal to the average self-diffusion process, we provide a formulation for this process of a voxel in terms of microstructure parameters of white matters. Several simulation results based on the proposed FEM method are given to demonstrate its flexibility and accuracy.

Quantification of self-diffusion process in brain white matter tracts via numerical PDE simulation

The performances of direct sequence-code division multiple access (DS-CDMA), multicarrier-CDMA (MC-CDMA) and multicarrier-direct sequence-CDMA (MC-DS-CDMA) systems under different channel conditions are compared in this work. In a frequency-selective slowly fading channel, MC-CDMA and MC-DS-CDMA outperform DS-CDMA, since the former two systems partition the frequency band into sub-channels, each of which has a nearly constant frequency response. Thus, MC-CDMA and MC-DS-CDMA do not suffer much from the multipath effect. The performance of MC-CDMA and MC-DS-CDMA can be further differentiated in severe fading conditions. In a frequency-selective fast fading channel, the larger spreading ratio of MC-DS-CDMA in the time domain prevents the chip duration of a sub-carrier from being longer than the channel coherence time. Hence, the sub-carrier orthogonality is maintained in MC-DS-CDMA, leading to its better performance in this case.

C.-C. Jay Kuo

Papers

Image compression using multiresolution morphological decomposition

FOR/SOR video coding with super macroblock and inter-frame stripe prediction

Compressed bit stream classification using VQ and GMM

Quantification of self-diffusion process in brain white matter tracts via numerical PDE simulation

Performance evaluation of data transmission over multicarrier CDMA systems