Networks for image acquisition, processing and display

The human visual system comprises layers of networks which sample, process, and code images. Understanding these networks is a valuable means of understanding human vision and of designing autonomous vision systems based on network processing. Ames Research Center has an ongoing program to develop computational models of such networks. The models predict human performance in detection of targets and in discrimination of displayed information. In addition, the models are artificial vision systems sharing properties with biological vision that has been tuned by evolution for high performance. Properties include variable density sampling, noise immunity, multi-resolution coding, and fault-tolerance. The research stresses analysis of noise in visual networks, including sampling, photon, and processing unit noises. Specific accomplishments include: models of sampling array growth with variable density and irregularity comparable to that of the retinal cone mosaic; noise models of networks with signal-dependent and independent noise; models of network connection development for preserving spatial registration and interpolation; multi-resolution encoding models based on hexagonal arrays (HOP transform); and mathematical procedures for simplifying analysis of large networks

An orthogonal oriented quadrature hexagonal image pyramid

An image pyramid has been developed with basis functions that are orthogonal, self-similar, and localized in space, spatial frequency, orientation, and phase. The pyramid operates on a hexagonal sample lattice. The set of seven basis functions consist of three even high-pass kernels, three odd high-pass kernels, and one low-pass kernel. The three even kernels are identified when rotated by 60 or 120 deg, and likewise for the odd. The seven basis functions occupy a point and a hexagon of six nearest neighbors on a hexagonal sample lattice. At the lowest level of the pyramid, the input lattice is the image sample lattice. At each higher level, the input lattice is provided by the low-pass coefficients computed at the previous level. At each level, the output is subsampled in such a way as to yield a new hexagonal lattice with a spacing sq rt 7 larger than the previous level, so that the number of coefficients is reduced by a factor of 7 at each level. The relationship between this image code and the processing architecture of the primate visual cortex is discussed

Predicting the readability of transparent text

Will a simple global masking model based on image detection be successful at predicting the readability of transparent text? Text readability was measured for two types of transparent text: additive (as occurs in head-up displays) and multiplicative (which occurs in see-through liquid crystal display virtual reality displays). Text contrast and background texture were manipulated. Data from two previous experiments were also included (one using very low contrasts on plain backgrounds, and the other using higher-contrast opaque text on both plain and textured backgrounds). All variables influenced readability in at least an interactive manner. When there were background textures, the global masking index (that combines text contrast and background root mean square contrast) was a good predictor of search times (r = 0.89). When the masking was adjusted to include the text pixels as well as the background pixels in computations of mean luminance and contrast variability, predictability improved further (r = 0.91)

The Spatial Standard Observer

The spatial standard observer is a computational model that provides a measure of the visibility of a target in a uniform background image or of the visual discriminability of two images. Standard observers have long been used in science and industry to quantify the discriminability of colors. Color standard observers address the spectral characteristics of visual stimuli, while the spatial standard observer (SSO), as its name indicates, addresses spatial characteristics. The SSO is based on a model of human vision. The SSO was developed in a process that included evaluation of a number of earlier mathematical models that address optical, physiological, and psychophysical aspects of spatial characteristics of human visual perception. Elements of the prior models are incorporated into the SSO, which is formulated as a compromise between accuracy and simplicity. The SSO operates on a digitized monochrome still image or on a pair of such images. The SSO consists of three submodels that operate sequentially on the input image(s): 1. A contrast model, which converts an input monochrome image to a luminance contrast image, wherein luminance values are expressed as excursions from, and normalized to, a mean; 2. A contrast-sensitivity-filter model that includes an oblique-effect filter (which accounts for the decline in contrast sensitivity at oblique viewing angles); and 3. A spatial summation model, in which responses are spatially pooled by raising each pixel to the power beta, adding the results, and raising the sum to the 1/b power. In this model, b=2.9 was found to be a suitable value. The net effect of the SSO is to compute a numerical measure of the perceptual strength of the single image, or of the visible difference (denoted the perceptual distance) between two images. The unit of a measure used in the SSO is the just noticeable difference (JND), which is a standard measure of perceptual discriminability. A target that is just visible has a measure of 1 JND

A visual detection model for DCT coefficient quantization

The discrete cosine transform (DCT) is widely used in image compression and is part of the JPEG and MPEG compression standards. The degree of compression and the amount of distortion in the decompressed image are controlled by the quantization of the transform coefficients. The standards do not specify how the DCT coefficients should be quantized. One approach is to set the quantization level for each coefficient so that the quantization error is near the threshold of visibility. Results from previous work are combined to form the current best detection model for DCT coefficient quantization noise. This model predicts sensitivity as a function of display parameters, enabling quantization matrices to be designed for display situations varying in luminance, veiling light, and spatial frequency related conditions (pixel size, viewing distance, and aspect ratio). It also allows arbitrary color space directions for the representation of color. A model-based method of optimizing the quantization matrix for an individual image was developed. The model described above provides visual thresholds for each DCT frequency. These thresholds are adjusted within each block for visual light adaptation and contrast masking. For given quantization matrix, the DCT quantization errors are scaled by the adjusted thresholds to yield perceptual errors. These errors are pooled nonlinearly over the image to yield total perceptual error. With this model one may estimate the quantization matrix for a particular image that yields minimum bit rate for a given total perceptual error, or minimum perceptual error for a given bit rate. Custom matrices for a number of images show clear improvement over image-independent matrices. Custom matrices are compatible with the JPEG standard, which requires transmission of the quantization matrix

The Fine Motor Skills and Cognition Test Batteries: Normative Data and Interdependencies

Fine motor skills and cognitive abilities are major contributors to crew performance on essentially all extravehicular and intra-vehicular activities during spaceflight. It is critical for the crews safety, and for mission productivity, to know if, and when, motor skills or cognitive abilities are compromised so that countermeasures may be introduced. NASA has developed two test batteries to measure and monitor astronaut cognitive and fine motor skills. The Cognition Test Battery contains 10 sub-tests that assess cognitive behaviors ranging from low level visual perception to high level decision-making. The Fine Motor Skills Test Battery contains 4 sub-tests that assess finger dexterity, manual dexterity and wrist-finger speed. This study sought to determine acceptable norms for both batteries in an astronaut-like population and to identify the extent to which fine motor skills contribute to cognitive test scores

Stroboscopic Image Modulation to Reduce the Visual Blur of an Object Being Viewed by an Observer Experiencing Vibration

A method and apparatus for reducing the visual blur of an object being viewed by an observer experiencing vibration. In various embodiments of the present invention, the visual blur is reduced through stroboscopic image modulation (SIM). A SIM device is operated in an alternating "on/off" temporal pattern according to a SIM drive signal (SDS) derived from the vibration being experienced by the observer. A SIM device (controlled by a SIM control system) operates according to the SDS serves to reduce visual blur by "freezing" (or reducing an image's motion to a slow drift) the visual image of the viewed object. In various embodiments, the SIM device is selected from the group consisting of illuminator(s), shutter(s), display control system(s), and combinations of the foregoing (including the use of multiple illuminators, shutters, and display control systems)

Calibration of a Visual System with Receptor Drop-out

Maloney and Ahumada [9] have proposed a network learning algorithm that allows the visual system to compensate for irregularities in the positions of its photoreceptors. Weights in the network are adjusted by a process tending to make the internal image representation translationinvariant. We report on the behavior of this translation-invariance algorithm calibrating a visual system that has lost receptors. To attain robust performance in the presence of aliasing noise, the learning adjustment was limited to the receptive field of output units whose receptors were lost. With this modification the translation-invariance learning algorithm provides a physiologically plausible model for solving the recalibration problem posed by retinal degeneration. 1.1 Introduction During the course of the degenerative disease retinitis pigmentosa (RP), patients experience progressive visual field loss, raised luminance and contrast thresholds, and nightblindness. Visual field loss typically begins in ..

A Fast DCT Block Smoothing Algorithm

mage compression based on quantizing the image in the discrete cosine transform (DCT) domain e can generate blocky artifacts in the output image. It is possible to reduce these artifacts and RMS rror by adjusting measures of block edginess and image roughness, while restricting the DCT s p coefficient values to values that would have been quantized to those of the compressed image. Thi aper presents a fast algorithm to replace our gradient search method for RMS error reduction and K image smoothing after adjustment of DCT coefficient amplitude. eywords: discrete cosine transform, image compression, artifact reduction, blocking artifacts, image smoothing. T INTRODUCTION he discrete cosine transform (DCT) is currently used in the MPEG and JPEG standards, and i 4 1-3 t also appears in proposed HDTV standards. We have been developing algorithms for improving the b quality of images that have been compressed by partitioning the image into blocks, converting each lock to DCT coefficie..

De-blocking DCT compressed images

mage compression based on quantizing the image in the discrete cosine transform (DCT) domain can c generate blocky artifacts in the output image. It is possible to reduce these artifacts and RMS error by orrecting DCT domain measures of block edginess and image roughness, while restricting the DCT coefficient values to values that would have been quantized to those of the compressed image. INTRODUCTION . T Lossy image compression in the DCT domain is achieved by the quantization of the DCT coefficients he quantization of a single coefficient in a single block causes the reconstructed image to differ from the e c original image by an error image proportional to the associated basis function in that block. When errors ar learly visible, the blockiness of the artifacts distinguishes them from the original image content, suggesting l s there may be a way of reducing these artifacts. This is an underconstrained vision problem that has no genera olution. For example, the original image coul..