This paper presents an algorithm for detecting multiple salient objects in images. The algorithm extends our previous algorithm which was designed to detect only a single salient object. The new algorithm employs five feature maps (lightness distance, color distance, contrast, sharpness, and edge strength), along with a new image-adaptive technique for estimating the usefulness of each feature map based on a local measure of cluster density. As we will demonstrate, our new version can successfully detect multiple salient objects on images for which the previous version did not succeed. Testing on subsets of images from two databases shows that the proposed algorithm performs well on a variety of images containing multiple salient objects.

This paper presents an algorithm designed to measure the local perceived sharpness in an image. Our method utilizes both spectral and spatial properties of the image: For each block, we measure the slope of the magnitude spectrum and the total spatial variation. These measures are then adjusted to account for visual perception, and then, the adjusted measures are combined via a weighted geometric mean. The resulting measure, i.e., S(3) (spectral and spatial sharpness), yields a perceived sharpness map in which greater values denote perceptually sharper regions. This map can be collapsed into a single index, which quantifies the overall perceived sharpness of the whole image. We demonstrate the utility of the S(3) measure for within-image and across-image sharpness prediction, no-reference image quality assessment of blurred images, and monotonic estimation of the standard deviation of the impulse response used in Gaussian blurring. We further evaluate the accuracy of S(3) in local sharpness estimation by comparing S(3) maps to sharpness maps generated by human subjects. We show that S(3) can generate sharpness maps, which are highly correlated with the human-subject maps.

This paper presents an algorithm for video quality assessment, spatiotemporal MAD (ST-MAD), which extends our previous image-based algorithm (MAD [1]) to take into account visual perception of motion artifacts. ST-MAD employs spatiotemporal “images” (STS images [2]) created by taking time-based slices of the original and distorted videos. Motion artifacts manifest in the STS images as spatial artifacts, which allows one to quantify motion-based distortion by using classical image-quality assessment techniques. ST-MAD estimates motion-based distortion by applying MAD's appearance-based model to compare the distorted video's STS images to the original video's STS images. This comparison is further adjusted by using optical-flow-derived weights designed to give greater precedence to fast-moving regions located toward the center of the video. Testing on the LIVE video database demonstrates that ST-MAD performs well in predicting video quality.

In this paper we present an algorithm which uses adaptive selection of low-level features for main subject detection. The algorithm first computes low-level features such as contrast and sharpness, each computed in a block-based fashion. Next, the algorithm quantifies the usefulness of each feature by using both statistical and geometric information measured across blocks. Finally, the saliency of each block is determined via a weighted linear combination of the features, where the weights are chosen based on each feature's estimated usefulness. Our results demonstrate that the adaptive nature of this algorithm allows it to perform competitively with other techniques, while maintaining very low computational complexity.

This paper presents the results of a computational experiment designed to investigate the extent to which metrics of image fidelity can be improved through knowledge of where humans tend to fixate in images. Five common metrics of image fidelity were augmented using two sets of fixation data, one set obtained under task-free viewing conditions and another set obtained when viewers were asked to judge image quality. The augmented metrics were then compared to subjective ratings of the images. The results show that most metrics can be improved using eye fixation data, but a greater improvement was found using fixations obtained in the task-free condition (task-free viewing).