Flat surfaces in our living environment to be used as replacements of a projection screen are not necessarily white. We propose a perceptual radiometric compensation method to counteract the effect of color projection surfaces on image appearance. It reduces color clipping while preserving the hue and brightness of images based on the anchoring property of human visual system. In addition, it considers the effect of chromatic adaptation on perceptual image quality and fixes the color distortion caused by non-white projection surfaces by properly shifting the color of the image pixels toward the complementary color of the projection surface. User ratings show that our method outperforms the existing methods in 974 out of 1020 subjective tests.

For a procam system consisting of a projector and a camera, the chroma distortion of an image projected on a non-white surface can be compensated by increasing the intensity of the complementary color of the projection surface. However, this inevitably trades image brightness for chroma correctness because the required intensity often outreaches the projector's dynamic range. In this paper, we propose a solution for the compensation problem using the anchoring theory of lightness perception. A notable feature of our technique is that it takes the chromatic adaptation and viewing condition into account, which leads to a better characterization of the image quality perceived by human eyes. As a result, the compensated image appears much closer to the original image. A computational framework for the tradeoff between brightness and chroma distortion is derived, and the performance of the proposed algorithm is verified by subjective experiments.

Switching the liquid crystal display (LCD) backlight of a portable multimedia device to a low power level saves energy but results in poor image quality especially for the low-luminance image areas. In this paper, we propose an image enhancement algorithm that overcomes such effects of dim LCD backlight by taking the human visual property into consideration. It boosts the luminance of image areas below the perceptual threshold while preserving the contrast of the other image areas. We apply the just noticeable difference theory and decompose an image into an HVS response layer and a background luminance layer. The boosting and compression processes, which enhance the visibility of the low-luminance image areas, are carried out in the background luminance layer to avoid luminance gradient reversal and over-compensation. The contrast of the processed image is further enhanced by exploiting the Craik-O'Brein-Cornsweet visual illusion. Experimental results are provided to show the performance of the proposed algorithm.

Unconscious attention shift has been proven to be automatic, effortless, and unresisting; however, whether it is applicable to images of complex scene remains to be verified. We propose a novel method for directing human visual attention by flashing a short-duration visual stimulus before presenting the image. This method directs viewer's visual attention to an arbitrary location in the image without engaging the viewer's awareness. Unlike previous studies using simple stimuli (e.g. simple geometric figures, image of uniform color, or artificial white-noise), we use complex color images for guiding unconscious visual attention in real-world. To our best knowledge, this is the first study demonstrating that short-duration cues presented at arbitrary locations can attract human visual attention in a complex scene. The proposed method is useful for applications that require efficient and unresisting attention shift to the target image area.

A procam system consists of a projector and a camera. In this paper, a radiometric compensation scheme is proposed for improving the projection quality of a procam system that uses a nearby non-white wall as the projection screen. The compensation scheme is capable of correcting the radiometric errors such as chroma and brightness distortions caused by the projection surface and the ambient light. It is also capable of compensating the effects of nonlinear spectral response (including vignetting) of the procam system. These important functions are achieved through a computational framework that optimizes the tradeoff between chroma and brightness distortions of the projected image and accelerates the computation of the penalty in the optimization process. Experimental results are shown to demonstrate the performance of the proposed radiometric compensation scheme.

Dimming the LCD backlight saves the battery power of a portable multimedia device but causes image quality degradation. In this paper, we propose an algorithm to overcome the effect of dim backlight on image quality. This algorithm enhances the backlight-scaled image by exploiting the human visual property. It performs a two-layer image decomposition based on the just noticeable difference theory and enhances the backlight-scaled image by luminance boosting and compression. Experimental results are provided to demonstrate the performance of the proposed algorithm.

A new computational scheme for visual attention modeling is proposed. It adopts both low-level and high-level features to predict visual attention from a video signal and fuses the features by using machine learning. We show that such a scheme is more robust than those using purely single level features. Unlike conventional techniques, our scheme is able to avoid perceptual mismatch between the estimated saliency and the actual human fixation. We show that selecting the representative training samples according to the fixation distribution improves the efficacy of regressive training. Experimental results are shown to demonstrate the advantages of the proposed scheme.

Visual attention, which is an important characteristic of human visual system, is a useful clue for image processing and compression applications in the real world. This paper proposes a computational scheme that adopts both low-level and high-level features to predict visual attention from video signal by machine learning. The adoption of low-level features (color, orientation, and motion) is based on the study of visual cells, and the adoption of the human face as a high-level feature is based on the study of media communications. We show that such a scheme is more robust than those using purely single low- or high-level features. Unlike conventional techniques, our scheme is able to learn the relationship between features and visual attention to avoid perceptual mismatch between the estimated salience and the actual human fixation. We also show that selecting the representative training samples according to the fixation distribution improves the efficacy of regressive training. Experimental results are shown to demonstrate the advantages of the proposed scheme.

It is often required to map the radiances of a real scene to a smaller dynamic range so that the image can be properly displayed. However, most such algorithms suffer from the halo artifact or require manual parameter tweaking that is often a tedious process for the user. We propose an automatic algorithm for high dynamic range compression based on the properties of human visual system. The algorithm is performed in the gradient domain to avoid the halo artifact. It automates the parameter adjustment process while preserving the image details. Performance comparison is provided to illustrate the advantages of the proposed algorithm.

One common way to extend the battery life of a portable device is to reduce the LCD backlight intensity. In contrast to previous approaches that minimize the power consumption by adjusting the backlight intensity frame by frame to reach a specified image quality, the proposed method optimizes the image quality for a given backlight intensity. Image is enhanced by performing brightness compensation and local contrast enhancement. For brightness compensation, global image statistics and backlight level are considered to maintain the overall brightness of the image. For contrast enhancement, the local contrast property of human visual system (HVS) is exploited to enhance the local image details. In addition, a brightness prediction scheme is proposed to speed up the algorithm for display of video sequences. Experimental results are presented to show the performance of the algorithm.

Maintaining image quality under various lighting conditions is critical to portable multimedia devices. In this paper, an image enhancement algorithm is proposed to deal with extremely dim LCD backlight (or strong ambient light) under which the image often becomes imperceptible. The crux of our idea is to keep the detail content of the image in the visible luminance range. To do that, a two-layer image decomposition architecture based on the just noticeable difference (JND) theory is proposed for extracting the details, and a boosting scheme is applied to the details that are in the low intensity regions of the image. Experimental results are provided to show the superiority of the proposed algorithm.