No-reference image quality assessment in contourlet domain

The target of no-reference (NR) image quality assessment (IQA) is to establish a computational model to predict the visual quality of an image. The existing prominent method is based on natural scene statistics (NSS). It uses the joint and marginal distributions of wavelet coefficients for IQA. However, this method is only applicable to JPEG2000 compressed images. Since the wavelet transform fails to capture the directional information of images, an improved NSS model is established by contourlets. In this paper, the contourlet transform is utilized to NSS of images, and then the relationship of contourlet coefficients is represented by the joint distribution. The statistics of contourlet coefficients are applicable to indicate variation of image quality. In addition, an image-dependent threshold is adopted to reduce the effect of content to the statistical model. Finally, image quality can be evaluated by combining the extracted features in each subband nonlinearly. Our algorithm is trained and tested on the LIVE database II. Experimental results demonstrate that the proposed algorithm is superior to the conventional NSS model and can be applied to different distortions. © 2009 Elsevier B.V. All rights reserved.

Perception while watching movies:effects of physical screen size and scene type

Over the last decade, television screens and display monitors have increased in size considerably, but has this improved our televisual experience? Our working hypothesis was that the audiences adopt a general strategy that “bigger is better.” However, as our visual perceptions do not tap directly into basic retinal image properties such as retinal image size (C. A. Burbeck, 1987), we wondered whether object size itself might be an important factor. To test this, we needed a task that would tap into the subjective experiences of participants watching a movie on different-sized displays with the same retinal subtense. Our participants used a line bisection task to self-report their level of “presence” (i.e., their involvement with the movie) at several target locations that were probed in a 45-min section of the movie “The Good, The Bad, and The Ugly.” Measures of pupil dilation and reaction time to the probes were also obtained. In Experiment 1, we found that subjective ratings of presence increased with physical screen size, supporting our hypothesis. Face scenes also produced higher presence scores than landscape scenes for both screen sizes. In Experiment 2, reaction time and pupil dilation results showed the same trends as the presence ratings and pupil dilation correlated with presence ratings, providing some validation of the method. Overall, the results suggest that real-time measures of subjective presence might be a valuable tool for measuring audience experience for different types of (i) display and (ii) audiovisual material.

Natural images dominate in binocular rivalry

Ecological approaches to perception have demonstrated that information encoding by the visual system is informed by the natural environment, both in terms of simple image attributes like luminance and contrast, and more complex relationships corresponding to Gestalt principles of perceptual organization. Here, we ask if this optimization biases perception of visual inputs that are perceptually bistable. Using the binocular rivalry paradigm, we designed stimuli that varied in either their spatiotemporal amplitude spectra or their phase spectra. We found that noise stimuli with “natural” amplitude spectra (i.e., amplitude content proportional to 1/f, where f is spatial or temporal frequency) dominate over those with any other systematic spectral slope, along both spatial and temporal dimensions. This could not be explained by perceived contrast measurements, and occurred even though all stimuli had equal energy. Calculating the effective contrast following attenuation by a model contrast sensitivity function suggested that the strong contrast dependency of rivalry provides the mechanism by which binocular vision is optimized for viewing natural images. We also compared rivalry between natural and phase-scrambled images and found a strong preference for natural phase spectra that could not be accounted for by observer biases in a control task. We propose that this phase specificity relates to contour information, and arises either from the activity of V1 complex cells, or from later visual areas, consistent with recent neuroimaging and single-cell work. Our findings demonstrate that human vision integrates information across space, time, and phase to select the input most likely to hold behavioral relevance.

The role of local luminance amplitude in the interpretation of shape-from-shading in textured surfaces

Luminance changes within a scene are ambiguous; they can indicate reflectance changes, shadows, or shading due to surface undulations. How does vision distinguish between these possibilities? When a surface painted with an albedo texture is shaded, the change in local mean luminance (LM) is accompanied by a similar modulation of the local luminance amplitude (AM) of the texture. This relationship does not necessarily hold for reflectance changes or for shading of a relief texture. Here we concentrate on the role of AM in shape-from-shading. Observers were presented with a noise texture onto which sinusoidal LM and AM signals were superimposed, and were asked to indicate which of two marked locations was closer to them. Shape-from-shading was enhanced when LM and AM co-varied (in-phase), and was disrupted when they were out-of-phase. The perceptual differences between cue types (in-phase vs out-of-phase) were enhanced when the two cues were present at different orientations within a single image. Similar results were found with a haptic matching task. We conclude that vision can use AM to disambiguate luminance changes. LM and AM have a positive relationship for rendered, undulating, albedo textures, and we assess the degree to which this relationship holds in natural images. [Supported by EPSRC grants to AJS and MAG].

Effects of the rate of formant-frequency variation on the grouping of formants in speech perception

How speech is separated perceptually from other speech remains poorly understood. Recent research suggests that the ability of an extraneous formant to impair intelligibility depends on the modulation of its frequency, but not its amplitude, contour. This study further examined the effect of formant-frequency variation on intelligibility by manipulating the rate of formant-frequency change. Target sentences were synthetic three-formant (F1?+?F2?+?F3) analogues of natural utterances. Perceptual organization was probed by presenting stimuli dichotically (F1?+?F2C?+?F3C; F2?+?F3), where F2C?+?F3C constitute a competitor for F2 and F3 that listeners must reject to optimize recognition. Competitors were derived using formant-frequency contours extracted from extended passages spoken by the same talker and processed to alter the rate of formant-frequency variation, such that rate scale factors relative to the target sentences were 0, 0.25, 0.5, 1, 2, and 4 (0?=?constant frequencies). Competitor amplitude contours were either constant, or time-reversed and rate-adjusted in parallel with the frequency contour. Adding a competitor typically reduced intelligibility; this reduction increased with competitor rate until the rate was at least twice that of the target sentences. Similarity in the results for the two amplitude conditions confirmed that formant amplitude contours do not influence across-formant grouping. The findings indicate that competitor efficacy is not tuned to the rate of the target sentences; most probably, it depends primarily on the overall rate of frequency variation in the competitor formants. This suggests that, when segregating the speech of concurrent talkers, differences in speech rate may not be a significant cue for across-frequency grouping of formants.

Human sensitivity to phase perturbations in natural images:A statistical framework

Fourier-phase information is important in determining the appearance of natural scenes, but the structure of natural-image phase spectra is highly complex and difficult to relate directly to human perceptual processes. This problem is addressed by extending previous investigations of human visual sensitivity to the randomisation and quantisation of Fourier phase in natural images. The salience of the image changes induced by these physical processes is shown to depend critically on the nature of the original phase spectrum of each image, and the processes of randomisation and quantisation are shown to be perceptually equivalent provided that they shift image phase components by the same average amount. These results are explained by assuming that the visual system is sensitive to those phase-domain image changes which also alter certain global higher-order image statistics. This assumption may be used to place constraints on the likely nature of cortical processing: mechanisms which correlate the outputs of a bank of relative-phase-sensitive units are found to be consistent with the patterns of sensitivity reported here.

Effects of differences in fundamental frequency on across-formant grouping in speech perception

In an isolated syllable, a formant will tend to be segregated perceptually if its fundamental frequency (F0) differs from that of the other formants. This study explored whether similar results are found for sentences, and specifically whether differences in F0 (?F0) also influence across-formant grouping in circumstances where the exclusion or inclusion of the manipulated formant critically determines speech intelligibility. Three-formant (F1 + F2 + F3) analogues of almost continuously voiced natural sentences were synthesized using a monotonous glottal source (F0 = 150 Hz). Perceptual organization was probed by presenting stimuli dichotically (F1 + F2C + F3; F2), where F2C is a competitor for F2 that listeners must resist to optimize recognition. Competitors were created using time-reversed frequency and amplitude contours of F2, and F0 was manipulated (?F0 = ±8, ±2, or 0 semitones relative to the other formants). Adding F2C typically reduced intelligibility, and this reduction was greatest when ?F0 = 0. There was an additional effect of absolute F0 for F2C, such that competitor efficacy was greater for higher F0s. However, competitor efficacy was not due to energetic masking of F3 by F2C. The results are consistent with the proposal that a grouping “primitive” based on common F0 influences the fusion and segregation of concurrent formants in sentence perception.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.