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

The block DCT transform plays an important role in the JPEG still image compression standard. In this research, we investigate the replacement of the DCT block transform in the JPEG with the fully-decomposed wavelet packet transform while keeping all other building blocks the same with the objective to improve the performance of the JPEG. Different wavelet packet bases have been used in experiments. Most of them give better performance than that of the DCT-based JPEG scheme in the mean square error measure as well as visual appearance.

JPEG-based image compression scheme using fully decomposed wavelet packet transform

This work proposes the use of a unitary modulation scheme to enhance physical layer secrecy in multi-antenna wireless systems. The proposed scheme takes advantage of having only channel state information at the transmitter (CSIT) but not at the receiver and the eavesdropper. With CSIT, the transmitter can first take the LQ or singular value decomposition of the channel matrix and then replace its unitary component by an information-bearing unitary matrix symbol. When the SNR is sufficiently high, the legitimate receiver is able to extract the unitary matrix by exploiting the uniqueness of the matrix decomposition. But the eavesdropper is incapable of doing so due to the ambiguity caused by the unknown unitary rotation on its channel, which ensures zero information leakage to the eavesdropper when the channels are independent. A detection scheme based on the chordal distance is proposed, and the achievable secrecy rate is derived and shown to depend only on the mutual information of the main channel. The simulation results demonstrate the effectiveness and secrecy advantages of our proposed unitary modulation scheme.

Unitary modulation for secrecy enhancement in multi-antenna wireless systems with only CSIT

An efficient 3D scene flow estimation method called PointFlowHop is proposed in this work. PointFlowHop takes two consecutive point clouds and determines the 3D flow vectors for every point in the first point cloud. PointFlowHop decomposes the scene flow estimation task into a set of subtasks, including ego-motion compensation, object association and object-wise motion estimation. It follows the green learning (GL) pipeline and adopts the feedforward data processing path. As a result, its underlying mechanism is more transparent than deep-learning (DL) solutions based on end-to-end optimization of network parameters. We conduct experiments on the stereoKITTI and the Argoverse LiDAR point cloud datasets and demonstrate that PointFlowHop outperforms deep-learning methods with a small model size and less training time. Furthermore, we compare the Floating Point Operations (FLOPs) required by PointFlowHop and other learning-based methods in inference, and show its big savings in computational complexity.

PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds

Motivated by both Shapiro’s embedded zerotree wavelet (EZW) coding and Taubman’s layered zero coding (LZC) approaches, we propose an embedded DCT still image compression scheme in this work. The new method generates a single embedded bit stream for DCT coefficients according to their importance. It is demonstrated that the new method performs better than the JPEG standard in rate-distortion tradeoff.

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Embedded DCT Still Image Compression

The random early migration (REM) scheme was proposed in our previous work to balance the load of multiple media servers to decrease the average service delay. When an user request arrives, it is randomly directed to a media server that has the designated video content cached on. When the load of this server exceeds a preset threshold, REM is executed by choosing one of its in-service requests and migrating it to another media server with a certain probability, where the exact probability is a function of the service load. We introduce a state matrix representation that stores the service load information of each media server and plays an important role in the determination of migration paths. All possible state matrices can be mapped to a vector space called the state matrix space (SMS). With SMS, we can analyze the performance of VoD systems such as the failure rate and service delay, and these derived results are verified by numerical experiments. It is demonstrated that REM outperforms the normal migration scheme with shorter service delay and lower failure rates.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

C.-C. Jay Kuo

Papers

JPEG-based image compression scheme using fully decomposed wavelet packet transform

Unitary modulation for secrecy enhancement in multi-antenna wireless systems with only CSIT

PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds

Embedded DCT Still Image Compression

Performance Analysis of Request Migration in Video-on-Demand Systems