Subpixel rendering

About: Subpixel rendering is a research topic. Over the lifetime, 3885 publications have been published within this topic receiving 82789 citations.

Systems and methods for selective handling of out-of-gamut color conversions

Abstract: Displays systems and methods for selectively reducing or compressing image data values within an image are recited. Display systems transform input image data from one input gamut hull or space to another target gamut hull or space that is substantially defined by different subpixel repeating groups comprising the display. Display systems described herein comprise a selective compression unit, said unit surveying said input image data to produce intermediate image data based upon a segmentation of the input image according to a parameter. Suitable parameters for segmenting the image include one or more of the following: spatial segments, chromatic segments or temporal segments. A selective compression amount may be determined so as to substantially maintain local contrast of the image data within a given segment.

A locally quadratic model of the motion estimation error criterion function and its application to subpixel interpolations

Abstract: Accurate motion estimation is essential to effective motion compensated video signal processing, and subpixel resolutions are required for high quality applications. It is observed that around the optimum point of the motion estimation process the error criterion function is well modeled as a quadratic function with respect to the motion vector offsets. This locally quadratic functional model decomposes the motion estimation optimization at subpixel resolutions into a two-stage pipelinable processes: full-search at full-pixel resolution and interpolation at any subpixel resolution. Practical approximation formulas lead to the explicit computations of both motion vectors and error criterion functional values at subpixel resolutions.

Enhanced Subpixel Mapping With Spatial Distribution Patterns of Geographical Objects

Abstract: This paper proposes spatial distribution pattern-based subpixel mapping (SPMS) as a novel subpixel mapping (SPM) strategy. It separately considers spatial distribution patterns of different types of geographical objects. Initially, it classifies geographical objects into areal, linear, and point patterns according to their spatially geometric characteristics. For the different patterns, SPMS uses the vectorial boundary-based SPM algorithm with the spatial dependence assumption to deal with areal objects, the linear template matching-based SPM algorithm for linear objects, and the spatial pattern consistency matching-based SPM algorithm for point objects. The three patterns are integrated to generate a subpixel map. An artificially created image and two remotely sensed images were used to evaluate the performance of SPMS. The results were compared with a traditional hard classifier and seven existing SPM methods. The experimental results demonstrated that SPMS performed better than the hard classification and traditional SPM methods, particularly when dealing with linear and point objects.

Super resolution optofluidic microscopes for 2d and 3d imaging

Abstract: A super resolution optofluidic microscope device comprises a body defining a fluid channel having a longitudinal axis and includes a surface layer proximal the fluid channel. The surface layer has a two-dimensional light detector array configured to receive light passing through the fluid channel and sample a sequence of subpixel shifted projection frames as an object moves through the fluid channel. The super resolution optofluidic microscope device further comprises a processor in electronic communication with the two-dimensional light detector array. The processor is configured to generate a high resolution image of the object using a super resolution algorithm, and based on the sequence of subpixel shifted projection frames and a motion vector of the object.

Sub-pixel accuracy location estimation from digital signals

Abstract: Identification of the spatial location of images in 1-D, 2-D, or even 3-D situations is often a requirement from sampled and digitized images which refer to regular independent linearly distributed pixels. The high-precision evaluation of the position requires interpolation calculations between the spatial samples to obtain a precision better than the geometrical pitch of the elementary pixels. Two calculation methods are discussed to attain such a subpixel accuracy: The first one proceeds from the well-known method of calculating the "centroid" of the samples in the direct space; the second one relies on the retrieval of the Fourier phase frequency dependence in the frequency domain. Both specifications are evaluated and the specific case of a 3-D signal is discussed. Both methods are independent of the analytical model for the function.