1632150
Local Phase Coherence and the Perception of Blur
Humans are able to detect blurring of visual images, but the mechanism by which they do so is not clear. A traditional view is that a blurred image looks "unnatural" because of the reduction in energy (either globally or locally) at high frequencies. In this paper, we propose that the disruption of local phase can provide an alternative explanation for blur perception. We show that precisely localized features such as step edges result in strong local phase coherence structures across scale and space in the complex wavelet transform domain, and blurring causes loss of such phase coherence. We propose a technique for coarse-to-fine phase prediction of wavelet coefficients, and observe that (1) such predictions are highly effective in natural images, (2) phase coherence increases with the strength of image features, and (3) blurring disrupts the phase coherence relationship in images. We thus lay the groundwork for a new theory of perceptual blur estimation, as well as a variety of algorithms for restoration and manipulation of photographic images.
5251967
Foveation scalable video coding with automatic fixation selection
Image and video coding is an optimization problem. A successful image and video coding algorithm delivers a good tradeoff between visual quality and other coding performance measures, such as compression, complexity, scalability, robustness, and security. In this paper, we follow two recent trends in image and video coding research. One is to incorporate human visual system (HVS) models to improve the current state-of-the-art of image and video coding algorithms by better exploiting the properties of the intended receiver. The other is to design rate scalable image and video codecs, which allow the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. Specifically, we propose a foveation scalable video coding (FSVC) algorithm which supplies good quality-compression performance as well as effective rate scalability. The key idea is to organize the encoded bitstream to provide the best decoded video at an arbitrary bit rate in terms of foveated visual quality measurement. A foveation-based HVS model plays an important role in the algorithm. The algorithm is adaptable to different applications, such as knowledge-based video coding and video communications over time-varying, multiuser and interactive networks.
6449041
Adaptive frame prediction for foveation scalable video coding
Embedded rate scalable video coding allows for the extraction of coded visual information at continuously varying bit rates from a single compressed bitstream. This is a very attractive feature for many multimedia communication applications. Motion estimation (ME) /motion compensation (MC) techniques are widely employed in various video coding systems to reduce temporal information redundancy. One of the major challenging problems in ME/MC based rate scalable video coding is how to generate the prediction frame from the previous frame to match the current frame. This problem is more difficult in rate scalable coding than in fixed rate coding because the decoding data rate is unavailable to the encoder. We propose an adaptive frame prediction scheme for foveation scalable video coding (FSVC), which is a new video coding algorithm that combines a foveation-based human visual system (HVS) model with a wavelet-based rate scalable coding algorithm. The new frame prediction algorithm provides an adaptive mechanism to control the prediction errors while reduce error propagation.
8060733
Embedded foveation image coding
The human visual system (HVS) is highly space-variant in sampling, coding, processing, and understanding. The spatial resolution of the HVS is highest around the point of fixation (foveation point) and decreases rapidly with increasing eccentricity. By taking advantage of this fact, it is possible to remove considerable high-frequency information redundancy from the peripheral regions and still reconstruct a perceptually good quality image. Great success has been obtained previously by a class of embedded wavelet image coding algorithms, such as the embedded zerotree wavelet (EZW) and the set partitioning in hierarchical trees (SPIHT) algorithms. Embedded wavelet coding not only provides very good compression performance, but also has the property that the bitstream can be truncated at any point and still be decoded to recreate a reasonably good quality image. In this paper, we propose an embedded foveation image coding (EFIC) algorithm, which orders the encoded bitstream to optimize foveated visual quality at arbitrary bit-rates. A foveation-based image quality metric, namely, foveated wavelet image quality index (FWQI), plays an important role in the EFIC system. We also developed a modified SPIHT algorithm to improve the coding efficiency. Experiments show that EFIC integrates foveation filtering with foveated image coding and demonstrates very good coding performance and scalability in terms of foveated image quality measurement.
207761262
Image quality assessment: from error visibility to structural similarity
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/.
17056192
Automatic Alignment of High-Resolution NMR Spectra Using a Bayesian Estimation Approach
Nuclear magnetic resonance (NMR) spectral analysis has recently become one of the major means for the detection and recognition of metabolic changes of disease state, physiological alteration, and natural biological variation. For the pattern recognition tasks in which two or more NMR spectra need to be compared, it is critical to properly align the spectra for the subsequent pattern recognition analysis. Previous spectral alignment methods do not consider any baseline intensity variation between the spectra and disregard the effect of noise. Here we formulate the spectra alignment problem in a Bayesian statistical framework, which allows us to simultaneously and efficiently estimate the spectral shift and the baseline intensity variation in the existence of independent additive noise. Experimental results with real high-resolution NMR spectral data from human plasma demonstrate the effectiveness and robustness of the proposed approach
31361781
Adaptive Processing of Tree-Structure Image Representation
Much research on image analysis and processing has been carried out for the last few decades. However, it is still challenging to represent the image contents effectively and satisfactorily. In this paper, a segmentation-free tree-structure image representation is presented. In order to learn the structure representation, a back-propagation through structure (BPTS) algorithm is adopted. Experiments on plant image classification and retrieval refining using only six visual features were conducted on a plant image database and a natural scene image database, respectively. Encouraging results have been achieved.
10182592
A Software Component Model and Its Preliminary Formalisation
A software component model should define what components are, and how they can be composed. That is, it should define a theory of components and their composition. Current software component models tend to use objects or port-connector type architectural units as components, with method calls and port-to-port connections as composition mechanisms. However, these models do not provide a proper composition theory, in particular for key underlying concepts such as encapsulation and compositionality. In this paper, we outline our notion of these concepts, and give a preliminary formalisation of a software component model that embodies these concepts.
1885677
Learning nonlinear multiregression networks based on evolutionary computation
This paper describes a novel knowledge discovery and data mining framework dealing with nonlinear interactions among domain attributes. Our network-based model provides an effective and efficient reasoning procedure to perform prediction and decision making. Unlike many existing paradigms based on linear models, the attribute relationship in our framework is represented by nonlinear nonnegative multiregressions based on the Choquet integral. This kind of multiregression is able to model a rich set of nonlinear interactions directly. Our framework involves two layers. The outer layer is a network structure consisting of network elements as its components, while the inner layer is concerned with a particular network element modeled by Choquet integrals. We develop a fast double optimization algorithm (FDOA) for learning the multiregression coefficients of a single network element. Using this local learning component and multiregression-residual-cost evolutionary programming (MRCEP), we propose a global learning algorithm, called MRCEP-FDOA, for discovering the network structures and their elements from databases. We have conducted a series of experiments to assess the effectiveness of our algorithm and investigate the performance under different parameter combinations, as well as sizes of the training data sets. The empirical results demonstrate that our framework can successfully discover the target network structure and the regression coefficients.
28890
Extension of Lower Probabilities and Coherence of Belief Measures
Coherence is an important concept which is introduced and discussed in a new mathematics branch, Imprecise Probability Theory. By using the Choquet integral, belief measures can be extended to be coherent lower previsions on the linear space consisting of all bounded functions. As a special case, we establish that all belief measures are coherent imprecise probabilities.
33403859
Area-power-time efficient pipeline-interleaved architectures for wave digital filters
Various forms of pipelining are explored for low power implementation of lattice wave digital filters realized with 3-port adaptors. In these filters, the time performance of pipelining is constrained by recursion. Using the fastest, block pipelined architecture as a reference point, it is shown that additional levels of pipelining can be applied to reduce the power consumption, at the expense of slightly changing the maximum sample rate. In one case power is reduced by 65% with only a modest speed penalty. Area increases due to additional pipeline registers can be more than offset if the consequent interleaving capability is utilized.