Visual Comfort Assessment for Stereoscopic Image Retargeting

In recent years, visual comfort assessment (VCA) for 3D/stereoscopic content has aroused extensive attention. However, much less work has been done on the perceptual evaluation of stereoscopic image retargeting. In this paper, we first build a Stereoscopic Image Retargeting Database (SIRD), which contains source images and retargeted images produced by four typical stereoscopic retargeting methods. Then, the subjective experiment is conducted to assess four aspects of visual distortion, i.e. visual comfort, image quality, depth quality and the overall quality. Furthermore, we propose a Visual Comfort Assessment metric for Stereoscopic Image Retargeting (VCA-SIR). Based on the characteristics of stereoscopic retargeted images, the proposed model introduces novel features like disparity range, boundary disparity as well as disparity intensity distribution into the assessment model. Experimental results demonstrate that VCA-SIR can achieve high consistency with subjective perception

Shape from periodic texture using the eigenvectors of local affine distortion

This paper shows how the local slant and tilt angles of regularly textured curved surfaces can be estimated directly, without the need for iterative numerical optimization, We work in the frequency domain and measure texture distortion using the affine distortion of the pattern of spectral peaks. The key theoretical contribution is to show that the directions of the eigenvectors of the affine distortion matrices can be used to estimate local slant and tilt angles of tangent planes to curved surfaces. In particular, the leading eigenvector points in the tilt direction. Although not as geometrically transparent, the direction of the second eigenvector can be used to estimate the slant direction. The required affine distortion matrices are computed using the correspondences between spectral peaks, established on the basis of their energy ordering. We apply the method to a variety of real-world and synthetic imagery

The ACS Virgo Cluster Survey IV: Data Reduction Procedures for Surface Brightness Fluctuation Measurements with the Advanced Camera for Surveys

The Advanced Camera for Surveys (ACS) Virgo Cluster Survey is a large program to image 100 early-type Virgo galaxies using the F475W and F850LP bandpasses of the Wide Field Channel of the ACS instrument on the Hubble Space Telescope (HST). The scientific goals of this survey include an exploration of the three-dimensional structure of the Virgo Cluster and a critical examination of the usefulness of the globular cluster luminosity function as a distance indicator. Both of these issues require accurate distances for the full sample of 100 program galaxies. In this paper, we describe our data reduction procedures and examine the feasibility of accurate distance measurements using the method of surface brightness fluctuations (SBF) applied to the ACS Virgo Cluster Survey F850LP imaging. The ACS exhibits significant geometrical distortions due to its off-axis location in the HST focal plane; correcting for these distortions by resampling the pixel values onto an undistorted frame results in pixel correlations that depend on the nature of the interpolation kernel used for the resampling. This poses a major challenge for the SBF technique, which normally assumes a flat power spectrum for the noise. We investigate a number of different interpolation kernels and show through an analysis of simulated galaxy images having realistic noise properties that it is possible, depending on the kernel, to measure SBF distances using distortion-corrected ACS images without introducing significant additional error from the resampling. We conclude by showing examples of real image power spectra from our survey.Comment: ApJS, in press, complete version of the paper at the link: http://www.physics.rutgers.edu/~pcote/acs/publications.htm

Optimal modeling for complex system design

The article begins with a brief introduction to the theory describing optimal data compression systems and their performance. A brief outline is then given of a representative algorithm that employs these lessons for optimal data compression system design. The implications of rate-distortion theory for practical data compression system design is then described, followed by a description of the tensions between theoretical optimality and system practicality and a discussion of common tools used in current algorithms to resolve these tensions. Next, the generalization of rate-distortion principles to the design of optimal collections of models is presented. The discussion focuses initially on data compression systems, but later widens to describe how rate-distortion theory principles generalize to model design for a wide variety of modeling applications. The article ends with a discussion of the performance benefits to be achieved using the multiple-model design algorithms