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/.

/pdf/image-quality-assessment-from-error-visibility-to-structural-1rlwcqe34t.pdf

Image quality assessment: from error visibility to structural similarity

Multiresolution representations are effective for analyzing the information content of images. The properties of the operator which approximates a signal at a given resolution were studied. It is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2/sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions. In L/sup 2/(R), a wavelet orthonormal basis is a family of functions which is built by dilating and translating a unique function psi (x). This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a pyramidal algorithm based on convolutions with quadrature mirror filters. Wavelet representation lies between the spatial and Fourier domains. For images, the wavelet representation differentiates several spatial orientations. The application of this representation to data compression in image coding, texture discrimination and fractal analysis is discussed. >

/pdf/a-theory-for-multiresolution-signal-decomposition-the-530tw5pzq7.pdf

A theory for multiresolution signal decomposition: the wavelet representation

Introduction to a Transient World. Fourier Kingdom. Discrete Revolution. Time Meets Frequency. Frames. Wavelet Zoom. Wavelet Bases. Wavelet Packet and Local Cosine Bases. An Approximation Tour. Estimations are Approximations. Transform Coding. Appendix A: Mathematical Complements. Appendix B: Software Toolboxes.

http://ahvazdownload.persiangig.com/document/article/39.pdf

A wavelet tour of signal processing

The Psychophysics Toolbox is a software package that supports visual psychophysics. Its routines provide an interface between a high-level interpreted language (MATLAB on the Macintosh) and the video display hardware. A set of example programs is included with the Toolbox distribution.

/pdf/the-psychophysics-toolbox-5342v18ct2.pdf

The Psychophysics Toolbox.

While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, matching, energy-based, and phase-based methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques we implemented.

Performance of optical flow techniques

We previously proposed a flicker visibility metric for bright displays, based on psychophysical data collected at a high mean luminance. Here we extend the metric to other mean luminances. This extension relies on a linear relation between log sensitivity and critical fusion frequency, and a linear relation between critical fusion frequency and log retinal illuminance. Consistent with our previous metric, the extended flicker visibility metric is measured in just-noticeable differences (JNDs).

5.3: Extending the Flicker Visibility Metric to a Range of Mean Luminance

Watson and Ahumada (2008) described a template model of visual acuity based on an ideal-observer limited by optical filtering, neural filtering, and noise. They computed predictions for selected optotypes and optical aberrations. Here we compare this model's predictions to acuity data for six human observers, each viewing seven different optotype sets, consisting of one set of Sloan letters and six sets of Chinese characters, differing in complexity (Zhang, Zhang, Xue, Liu, & Yu, 2007). Since optical aberrations for the six observers were unknown, we constructed 200 model observers using aberrations collected from 200 normal human eyes (Thibos, Hong, Bradley, & Cheng, 2002). For each condition (observer, optotype set, model observer) we estimated the model noise required to match the data. Expressed as efficiency, performance for Chinese characters was 1.4 to 2.7 times lower than for Sloan letters. Efficiency was weakly and inversely related to perimetric complexity of optotype set. We also compared confusion matrices for human and model observers. Correlations for off-diagonal elements ranged from 0.5 to 0.8 for different sets, and the average correlation for the template model was superior to a geometrical moment model with a comparable number of parameters (Liu, Klein, Xue, Zhang, & Yu, 2009). The template model performed well overall. Estimated psychometric function slopes matched the data, and noise estimates agreed roughly with those obtained independently from contrast sensitivity to Gabor targets. For optotypes of low complexity, the model accurately predicted relative performance. This suggests the model may be used to compare acuities measured with different sets of simple optotypes.

Modeling Acuity for Optotypes Varying in Complexity

Spectral information preserved to extent possible. Technique developed to shrink or expand discrete input sequence of numbers into output sequence in manner preserving input spectrum up to Nyquist limit of smaller of two sequences. In case of expansion, technique also prevents introduction of spurious frequencies not present in input. While applicable to many kinds of data sampled at regular interval, particularly useful in processing and enhancement of images, where discrete sequences spatially ordered sets of picture-element brightness values. Image coarsened by resampling to reduce number of picture elements while preserving as much as possible of original image information. Used to generate intermediate images at small intervals between frames, thereby suppressing appearance of jerky motion caused by sudden jumps between frames at low frame rate.

Ideal Resampling Of Discrete Sequences

Motion blur is a significant display property for which accurate, valid, and robust measurement methods are needed. Recent motion blur measurements of a set of eight displays by a set of six measurement devices provided an opportunity to evaluate techniques of measurement and analysis. We found significant discrepancies between instruments, and variability within instruments. 1 Objective and Background Many modern display technologies, notably LCD, are subject to motion blur. Motion blur arises when the eye tracks a moving image, while the display presents individual frames that persist for significant fractions of a frame duration, or longer. As a result, the image is smeared across the retina during the frame duration. There have been a number of attempts to characterize motion blur in a systematic and meaningful way. Most of these involve estimating the width of an edge subjected to motion blur. This edge can be captured in any of three ways[1, 2]. The first method employs a pursuit camera that tracks a vertical edge (between two graylevels) as it moves horizontally across the screen. The camera is simulating the eye as it pursues the moving edge. The result, after averaging over time, is a picture of the blurred edge. After averaging over the vertical dimension (orthogonal to the motion), a one-dimensional waveform representing the cross-section of the blurred edge can be obtained. It describes relative luminance (a number proportional to luminance) as a function of horizontal position in pixels. We will call this the Moving Edge Spatial Profile (MESP). When recorded at several speeds of edge motion, the waveforms are usually found to correspond when the horizontal scale is divided by the speed. Therefore it is conventional to rescale the horizontal axis of the profile (pixels) by dividing by the speed (pixels/frame) to obtain a waveform that is a function of time (frames). We call this the Moving Edge Temporal Profile (METP). It is also conventional to characterize the width of the METP in terms of the time interval between 10% and 90% points of the curve. This quantity is called the Blur Edge Time (BET) and is reported in msec. The second method employs a stationary high-speed camera. With a sufficiently high frame rate, it is possible to capture a sequence of frames, that, with appropriate shifting and adding, can also yield a record of the MESP and thereby the METP. The highspeed camera avoids the mechanical challenges of the pursuit camera. The third method employs a fixed non-imaging detector such as a photodiode which measures the luminance over time as the display is switched from one graylevel to another. This temporal step response is then convolved with a pulse of duration equal to the hold time (for an LCD, typically one frame), to obtain another version of the METP [3]. This last method relies on an assumption that all pixels are independent. It has been

/pdf/17-1-comparison-of-motion-blur-measurement-methods-y9ya9mftbj.pdf

17.1: Comparison of Motion‐Blur Measurement Methods

Models that predict human performance on narrow classes of visual stimuli abound in the vision science literature. However, the vision and the applied imaging communities need robust general-purpose, rather than narrow, computational human visual system (HVS) models to evaluate image fidelity and quality and ultimately improve imaging algorithms. Of the general-purpose early HVS models that currently exist, direct model comparisons on the same data sets are rarely made. The Modelfest group was formed several years ago to solve these and other vision modeling issues. The group has developed a database of static spatial test images with threshold data that is posted on the WEB for modellers to use in HVS model design and testing. The first phase of data collection was limited to detection thresholds for static gray scale 2D images. The current effort will extend the database to include thresholds for selected grayscale 2D spatio-temporal image sequences. In future years, the database will be extended to include discrimination (masking) for dynamic, color and gray scale image sequences. The purpose of this presentation is to invite the Electronic Imaging community to participate in this effort and to inform them of the developing data set, which is available to all interested researchers. This paper presents the display specifications, psychophysical methods and stimulus definitions for the second phase of the project, spatio-temporal detection. The threshold data will be collected by each of the authors over the next year and presented on the WEB along with the stimuli.

Andrew B. Watson

Papers

5.3: Extending the Flicker Visibility Metric to a Range of Mean Luminance

Modeling Acuity for Optotypes Varying in Complexity

Ideal Resampling Of Discrete Sequences

17.1: Comparison of Motion‐Blur Measurement Methods

Extending the modelfest image/threshold database into the spatio-temporal domain