Predicting complexity perception of real world images

The aim of this work is to predict the complexity perception of real world images.We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images

Predicting complexity perception of real world images

The aim of this work is to predict the complexity perception of real world images.We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images

A multidistortion database for image quality

In this paper we introduce a multidistortion database, where 10 pristine color images have been simultaneously distorted by two types of distortions: blur and JPEG and noise and JPEG. The two datasets consist of respectively 350 and 400 images, and have been subjectively evaluated within two psycho-physical experiments. We here also propose two no reference multidistortion metrics, one for each of the two datasets, as linear combinations of no reference single distortion ones. The optimized weights of the combinations are obtained using particle swarm optimization. The different combinations proposed show good performance when correlated with the subjective scores of the multidistortion database

Genetic programming approach to evaluate complexity of texture images

We adopt genetic programming (GP) to define a measure that can predict complexity perception of texture images. We perform psychophysical experiments on three different datasets to collect data on the perceived complexity. The subjective data are used for training, validation, and test of the proposed measure. These data are also used to evaluate several possible candidate measures of texture complexity related to both low level and high level image features. We select four of them (namely roughness, number of regions, chroma variance, and memorability) to be combined in a GP framework. This approach allows a nonlinear combination of the measures and could give hints on how the related image features interact in complexity perception. The proposed complexity measure MGP exhibits Pearson correlation coefficients of 0.890 on the training set, 0.728 on the validation set, and 0.724 on the test set. MGP outperforms each of all the single measures considered. From the statistical analysis of different GP candidate solutions, we found that the roughness measure evaluated on the gray level image is the most dominant one, followed by the memorability, the number of regions, and finally the chroma variance

Complexity perception of texture images

Visual complexity perception plays an important role in the fields of both psychology and computer vision: it can be useful not only to investigate human perception but also to better understand the properties of the objects being perceived. In this paper we investigate the complexity perception of texture images. To this end we perform a psycho-physical experiment on real texture patches. The complexity of each image is assessed on a continuous scale. At the end of the evaluation, each observer indicates the criteria used to assess texture complexity. The most frequent criteria used are regularity, understandability, familiarity and edge density. As candidate complexity measures we consider thirteen image features and we correlate each of them with the subjective scores collected during the experiment. The performance of these correlations are evaluated in terms of Pearson correlation coefficients. The four measures that show the highest correlations are energy, edge density, compression ratio and a visual clutter measure, in accordance with the verbal descriptions collected by the questionnaire

Adaptive contrast enhancement for underexposed images

In the present article we focus on enhancing the contrast of images with low illumination that present large underexposed regions. For these particular images, when applying the standard contrast enhancement techniques, we also introduce noise over-enhancement within the darker regions. Even if both the contrast enhancement and denoising problems have been widely addressed within the literature, these two processing steps are, in general, independently considered in the processing pipeline. The goal of this work is to integrate contrast enhancement and denoise algorithms to proper enhance the above described type of images. The method has been applied to a proper database of underexposed images. Our results have been qualitatively compared before and after applying the proposed algorithm. \ua9 2011 SPIE-IS&T

Low Quality Image Enhancement Using Visual Attention

Low quality images are often corrupted by artifacts and generally need to be heavily processed to become visually pleasing. We present a modified version of unsharp masking that is able to perform image smoothing, while not only preserving but also enhancing the salient details in images. The premise supporting the work is that biological vision and image reproduction share common principles. The key idea is to process the image locally according to topographic maps obtained from a neurodynamical model of visual attention. In this way, the unsharp masking algorithm becomes local and adaptive, enhancing the edges differently according to human perception

Grouping strategies to improve the correlation between subjective and objective image quality data

The aim of our research is to specify experimentally and further model spatial frequency response functions, which quantify human sensitivity to spatial information in real complex images. Three visual response functions are measured: the isolated Contrast Sensitivity Function (iCSF), which describes the ability of the visual system to detect any spatial signal in a given spatial frequency octave in isolation, the contextual Contrast Sensitivity Function (cCSF), which describes the ability of the v isual system to detect a spatial signal in a given octave in an image and the contextual Visual Perception Function (VPF), which describes visual sensitivity to changes in suprathreshold contrast in an image. In this paper we present relevant background, along with our first attempts to derive experimentally and further model the VPF and CSFs. We examine the contrast detection and discrimination frameworks developed by Barten, which we find prov ide a sound starting position for our own modeling purposes. Progress is presented in the following areas: verification of the chosen model for detection and discrimination; choice of contrast metrics for defining contrast sensitivity; apparatus, laboratory set-up and imaging system characterization; stimuli acquisition and stimuli variations; spatial decomposition; methodology for subjective tests. Initial iCSFs are presented and compared with 'classical' findings that hav e used simple visual stimuli, as well as with more recent relevant work in the literature. \ua9 2013 SPIE-IS&T

Adaptive edge enhancement using a neurodynamical model of visual attention

A new approach for selective edge enhancement using unsharp masking is presented. This is based on the premise that biological vision and image reproduction share common principles. In the traditional approach the high frequency components of the image are emphasized, adding to the signal a constant fraction of its high-pass filtered version. The presence of a linear high-pass filter makes the system extremely sensitive to noise. In our approach, the high frequencies added to input image are weighted by a topographic map corresponding to visually salient regions, obtained by a neurodynamical model of visual attention. In this way, the unsharp masking algorithm becomes local and adaptive, enhancing differently the edges according to human perception