Clayton Brian Atkins

Optimal image scaling using pixel classification

We introduce a new approach to optimal image scaling called resolution synthesis (RS). In RS, the pixel being interpolated is first classified in the context of a window of neighboring pixels; and then the corresponding high-resolution pixels are obtained by filtering with coefficients that depend upon the classification. RS is based on a stochastic model explicitly reflecting the fact that pixels falls into different classes such as edges of different orientation and smooth textures. We present a simple derivation to show that RS generates the minimum mean-squared error (MMSE) estimate of the high-resolution image, given the low-resolution image. The parameters that specify the stochastic model must be estimated beforehand in a training procedure that we have formulated as an instance of the well-known expectation-maximization (EM) algorithm. We demonstrate that the model parameters generated during the training may be used to obtain superior results even for input images that were not used during the training.

Model-based color image sequence quantization

We investigate the display of color image sequences using a model-based approach to multilevel error diffusion. We extend Bouman and Kolpatziks technique for design of optimal filters to the temporal dimension. Our model for the human visual system accounts for the spatial and temporal frequency dependence of the contrast sensitivity of the luminance and chrominance channels. We observe an improvement in image quality over that yielded by frame-independent quantization, when the frame rate is sufficiently high to support temporal averaging by the human visual system.

Halftone postprocessing for improved rendition of highlights and shadows

Many binary halftoning algorithms tend to render ex- treme tones (i.e., very light or very dark tones) with objectionable dot distributions. To alleviate this artifact, we introduce a halftone post- processing algorithm called the Springs algorithm. The objective of Springs is to rearrange minority pixels in affected regions for a smoother, more attractive rendition. In this paper, we describe the Springs algorithm, and we show results which demonstrate its effec- tiveness. The heart of this algorithm is a simple dot-rearrangement heuristic which results in a more isotropic dot distribution. The ap- proach is to treat any well-isolated dot as if it were connected to neighboring dots by springs, and to move it to a location where the energy in the springs is a minimum. Applied to the whole image, this could degrade halftone appearance. However, Springs only moves dots in selected regions of the image. Pixels that are not minority pixels are not moved at all. Moreover, dot rearrangement is disabled on and around detected edges, since it could otherwise render those edges soft and diffuse.

Least-squares model-based video halftoning

A technique for halftoning video sequences is presented. First, an error metric is designed, incorporating psychophysical spatio-temporal contrast sensitivity data for the human visual system. An efficient strategy for producing halftone sequences that minimize the error is then developed. The technique is compared with video halftoning algorithms that do not consider the temporal aspect of the human visual system.

MagicPhotobook: designer inspired, user perfected photo albums

Computer-assisted photo album creation continues to be a challenging application as it requires integrated technical solutions to many difficult problems. Effective solutions must leverage both design knowledge and image understanding algorithms to automate time-consuming tasks like image selection, grouping, cropping, layout and background selection. At the same time, they should allow the user to cater to personal tastes by fine-tuning aspects of album appearance. MagicPhotobook is a photobook authoring system that takes steps in these directions by providing advances over prior solutions in the following areas: automatic image selection and theme-based image grouping; dynamic page layout; automatic cropping; automatic background selection; design-preserving background artwork transformation; and a simple yet powerful user interface for personalization.

Halftone postprocessing for improved highlight rendition

Many halftoning algorithms tend to render highlight regions with objectionable dot distributions. To alleviate this artifact, we introduce a halftone postprocessing algorithm called the Springs algorithm. The objective of Springs is to rearrange dots in affected regions for a smoother more attractive rendition. We describe the Springs algorithm, and we show results which demonstrate its effectiveness. The heart of this algorithm is a simple dot-rearrangement heuristic which results in a more isotropic dot distribution. The approach is to treat any well-isolated dot as if it were connected to neighboring dots by springs, and to move it to a location where the energy in the springs is a minimum. Applied to the whole image, this could degrade the halftone appearance. However, Springs only moves dots in selected regions of the image. To select these regions, Springs employs a segmentation scheme which is based on finding light regions which do not exhibit strong edge structures.