Human Visual Sensitivity Research Articles

The economics of motion perception and invariants of visual sensitivity

Neural systems face the challenge of optimizing their performance with limited resources, just as economic systems do. Here, we use tools of neoclassical economic theory to explore how a frugal visual system should use a limited number of neurons to optimize perception of motion. The theory prescribes that vision should allocate its resources to different conditions of stimulation according to the degree of balance between measurement uncertainties and stimulus uncertainties. We find that human vision approximately follows the optimal prescription. The equilibrium theory explains why human visual sensitivity is distributed the way it is and why qualitatively different regimes of apparent motion are observed at different speeds. The theory offers a new normative framework for understanding the mechanisms of visual sensitivity at the threshold of visibility and above the threshold and predicts large-scale changes in visual sensitivity in response to changes in the statistics of stimulation and system goals.

Inhibition of the Dopamine D1 Receptor Signaling by PSD-95

Dopamine D1 receptors play an important role in movement, reward, and learning and are implicated in a number of neurological and psychiatric disorders. These receptors are concentrated in dendritic spines of neurons, including the spine head and the postsynaptic density. D1 within spines is thought to modulate the local channels and receptors to control the excitability and synaptic properties of spines. The molecular mechanisms mediating D1 trafficking, anchorage, and function in spines remain elusive. Here we show that the synaptic scaffolding protein PSD-95 thought to play a role in stabilizing glutamate receptors in the postsynaptic density, interacts with D1 and regulates its trafficking and function. Interestingly, the D1-PSD-95 interaction does not require the well characterized domains of PSD-95 but is mediated by the carboxyl-terminal tail of D1 and the NH(2) terminus of PSD-95, a region that is recognized only recently to participate in protein-protein interaction. Co-expression of PSD-95 with D1 in mammalian cells inhibits the D1-mediated cAMP accumulation without altering the total expression level or the agonist binding properties of the receptor. The diminished D1 signaling is mediated by reduced D1 expression at the cell surface as a consequence of an enhanced constitutive, dynamin-dependent endocytosis. In addition, genetically engineered mice lacking PSD-95 show a heightened behavioral response to either a D1 agonist or the psychostimulant amphetamine. These studies demonstrate a role for a glutamatergic scaffold in dopamine receptor signaling and trafficking and identify a new potential target for the modulation of abnormal dopaminergic function.

Spatiotemporal Visual Considerations for Video Coding

Human visual sensitivity varies with not only spatial frequencies, but moving velocities of image patterns. Moreover, the loss of visual sensitivity due to object motions might be compensated by eye movement. Removing the psychovisual redundancies in both the spatial and temporal frequency domains facilitates an efficient coder without perceptual degradation. Motivated by this, a visual measure is proposed for the purpose of video compressions. The novelty of this analysis relies on combining three visual factors altogether: the motion attention model, unconstrained eye-movement incorporated spatiovelocity visual sensitivity model, and visual masking model. For each motion-unattended macroblock, the retinal velocity is evaluated so that discrete cosine transform coefficients to which the human visual system has low sensitivity are picked up with the aid of eye movement incorporated spatiovelocity visual model. Based on masking thresholds of those low-sensitivity coefficients, a spatiotemporal distortion masking measure is determined. Accordingly, quantization parameters at macroblock level for video coding are adjusted on the basis of this measure. Experiments conducted by H.264 exhibit the effectiveness of the proposed scheme in improving coding performance without picture quality degradation

No-reference JPEG-image quality assessment using GAP-RBF

In this paper, we present a novel no-reference (NR) method to assess the quality of JPEG-coded images using a sequential learning algorithm for growing and pruning radial basis function (GAP-RBF) network. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the functional relationship is approximated using GAP-RBF network. The advantage of using sequential learning algorithm is its capability to learn new samples without affecting the past learning. Further, the sequential learning algorithm requires minimal memory and computational effort. Experimental results prove that the prediction of the trained GAP-RBF network does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.

Experimental Results on Human Visual Sensitivity for Spatial Frequency of Digital Halftone Images

In this paper we add some new experimental results to our recent work and discuss them with respect to human visual sensitivity for spatial frequency. In our experiment, several dot pattern images and sinusoidal gratings are used to probe the capabilities of the visual system.We can see some characteristics of human perceptual system when we analyze our experimental results from the viewpoint of halftone dot size, shape and frequency.

Objective image quality measurement by local spatial‐frequency wavelet analysis

An essential determinant of the value of surrogate digital images is their quality. Image quality measurement has become crucial for most image processing applications. Over the past years, there have been many attempts to develop models or metrics for image quality that incorporate elements of human visual sensitivity. However, there is no current standard and objective definition of spectral image quality. This paper proposes a reliable automatic method for objective image quality measurement by local spatial‐frequency wavelet analysis. The analysis is performed locally by dividing an image into 64×64 pixel blocks, and transforming the data into wavelet domain and sub‐band image domain. A fast lifting wavelet algorithm is developed for computationally efficient spatial‐frequency deconstruction of images to extract the features of edges in sub‐band datasets. Wavelet analysis throughout the spatial‐frequency range with respect to noise, sharpness, brightness, contrast, and modulation transfer function (MTF) reveals more detailed information to relate the quality of an image to the interpretation by using an artificial neural network to optimize weightings and results in a promising quantitative measure of image quality. Experimental results from using this method for image quality measurement exhibit good correlation to subjective visual quality assessments. The proposed approach with fast computation provides a potential metric for reliable image quality quantification, allowing image quality to be automatically evaluated following image capture/compression, but prior to permanent storage in a database.

14.1: Resolution Artefacts in Multi-View Autostereoscopic 2D/3D Displays

tereoscopic displays suitable for the mass market typically rely on spatial multiplexing to present different views in different directions. To obtain the wide viewing freedom often requested by display users, the number of views presented is increased by the sacrifice of display resolution. The 3D mode of a spatially multiplexed display has reduced resolution. To assess optimum design parameters, it is necessary to investigate both the loss of resolution and the redistribution of the colour filter pattern in the 3D mode. The quantitative analysis of frequency structure using Nyquist boundaries is a well known technique for analysing the two dimensional distribution of sampling data points (8,9) . In order to compare such multi-view displays, both quantitative and qualitative comparisons have been made of resolution losses and pixel appearance. Specifically, 2.2 2D/3D panel performance has been assessed using Nyquist boundaries, human visual contrast sensitivity models and autostereoscopic display optical output simulations.

Facilitation of perceptual filling-in for spatio-temporal frequency of dynamic textures

Objects are perceived to fade and disappear within a few seconds under certain conditions when a small object surrounded by a moving texture is presented in human peripheral vision. This phenomenon is called perceptual filling-in or fading. Investigation of filling-in properties is important to understand visual information capture and processing. Previous studies have adopted filling-in time to evaluate the facilitation of filling-in. From this viewpoint, we propose a model of the filling-in process to address the phenomenon by which a small homogeneous area (filling-in target), which is surrounded by spatio-temporal frequency limited random-dot dynamic textures, is presented to an observer's peripheral vision (Proc. IC-EMBS2003). The model expresses target distinguishability from the surrounding texture. This study measured time to filling-in for various spatio-temporal frequencies of target-surrounding dynamic textures. Spatio-temporal frequency sensitivity of human vision was also estimated. Applying these results to the proposed model, it was suggested that M-channel pathway of LGN facilitates perceptual filling-in. In contrast, the P-channel pathway is assumed not to facilitate, but rather inhibit, filling-in

Organizing Spectral Image Database Using Self-Organizing Maps

Techniques for searching images from a spectral image database and calculating the distances between spectral images using different distance measures are proposed and the importance of the normalization that is based on the human visual sensitivity function is examined. The searching techniques are based on the use of one-and two-dimensional Self-Organizing Map (SOM). In the case of one-dimensional SOM, the Best Matching Unit (BMU) histogram is created for every spectral image of a database, and images are ordered according to the histogram dissimilarity. Two-dimensional SOM is trained by using spectral data and BMU-histograms as a training data and the distance between spectral images is defined based on the histogram dissimilarity and image locations on the map, respectively. The proposed techniques are useful in image search and the order of the database is different for spectral images and for spectral images weighted by human visual sensitivity function. The order of the database is also highly dependent on the used distance measure. The results using a real spectral image database are given.

MAGNITUDE SYSTEMS IN OLD STAR CATALOGUES

The current system of stellar magnitudes originally introduced by Hipparchus was strictly defined by Norman Pogson in 1856. He based his system on Ptolemy's star catalogue, the <italic>Almagest,</italic> recorded in about AD137, and defined the magnitude-intensity relationship on a logarithmic scale. Stellar magnitudes observed with the naked eye recorded in seven old star catalogues were analyzed in order to examine the visual magnitude systems. Although psychophysicists have proposed that human visual sensitivity follows a power-law scale, it is shown here that the degree of agreement is far better for a logarithmic scale than for a power-law scale. It is also found that light ratios in each star catalogue are nearly equal to 2.512, if the brightest (1^st magnitude) and the faintest (6^th magnitude and dimmer) stars are excluded from the study. This means that the visual magnitudes in the old star catalogues agree fully with Pogson's logarithmic scale.

Color image resolution conversion

In this paper, we look at the problem of spatially scaling color images. We focus on an approach that takes advantage of the human visual system's color spatial frequency sensitivity. The algorithm performs an efficient least-squares (LS) resolution conversion for the luminance channel and a low-complexity pixel replication/reduction in the chrominance channels. The performance of the algorithm is compared to a LS method in sRGB and CIELAB color spaces, as well as standard bilinear interpolation in sRGB space. The comparisons are made in terms of computational cost and color error in sCIELAB.

Steganography using multiple-base notational system and human vision sensitivity

This letter proposes a novel steganographic scheme that employs human vision sensitivity to hide a large amount of secret bits into a still image with a high imperceptibility. In this method, data to be embedded are converted into a series of symbols in a notation system with multiple bases. The specific bases used are determined by the degree of local variation of the pixel magnitudes in the host image so that pixels in busy areas can potentially carry more hidden data. Experimental results are given to show the advantage of this adaptive technique.

Some Characteristics on Human Visual Sensitivity for Spatial Frequency of Digital Halftone Images

In this paper we show some experimental results of image recognition, and discuss them from the viewpoint of human visual sensitivity for spatial frequency. We have been interested in how we see digital halftone images which consists of halftone dots.

COMPUTATIONAL VISUAL SENSITIVITY MODELS — A REVIEW

The Human Visual Systems (HVSs) is imperfect and contains highly selective visual acquisition sensors. Not all useful information presented to human eyes can be perceived. Generally, a technique to determine whether a signal can be perceived and how well it can be perceived is called human visual sensitivity analysis. More than one hundred years of psychophysical research on HVSs has revealed that human visual sensitivity is not only determined by local characteristics of visual contents (luminance, contrast orientation, spatial and temporal frequency), but also global modulatory factors (visual attention and motion suppression). In this paper, we provide a review on various factors that affect human visual sensitivity and on various computational models for human visual sensitivity. A comparative study on the performance of various visual sensitivity models by simulations is also reported in the paper. Subjective evaluation on noise-embedded video sequences confirms that the introduction of global modulatory factors does improve the performance of the Just Noticeable Difference (JND) profile used in noise shaping.

Evaluation of Graininess for Digital Halftone Images Based on Human Visual Sensitivity

In this paper we show some results of image recognition tests in which a testee looks at an image and tell if he/she can recognize a certain figure in it or not. When we study digital halftoning process, it is important to discuss the resolution of the human eye, or eye and brain, from the viewpoint of image recognition.

Vulnerability of pixel-value differencing steganography to histogram analysis and modification for enhanced security

The pixel-value differencing (PVD) steganography can embed a large amount of secret bits into a still image with high imperceptibility as it makes use of the characteristics of human vision sensitivity. However, a loophole exists in the PVD method. Unusual steps in the histogram of pixel differences reveal the presence of a secret message. An analyst can even estimate the length of hidden bits from the histogram. To enhance security, a modified scheme is proposed which avoids occurrence of the above-mentioned steps in the pixel difference histogram while preserving the advantage of low visual distortion of the PVD. The histogram-based steganalysis is therefore defeated.

Towards perceptual control of physically based spectral rendering

Realistic rendering research focuses on generating images that should be perceived the same way as a real scene. We will describe a physically based spectral framework where energy, materials and light field are wavelength dependent. To reduce the computational load we also make use of the high variations of the human vision sensitivity to adapt the accuracy of every computation. As no comprehensive human vision model exists, we use several types of perceptual control. In order to adapt computation accuracy we have also derived a simple but useful perceptual error estimate. This paper will introduce some ideas.

Optimum partition of two‐dimensional frequency band with human visual sensitivity for subband image coding

AbstractThis paper proposes a method of optimizing the partition of a two‐dimensional frequency band so that high‐quality reconstructed images are obtained by subband image coding. This partitioning is derived by minimizing the weighted quantization noise power with human visual sensitivity. To simplify the coding system, the structure of the filter bank is analyzed. The band partition parameters which makes the filter bank more efficient are selected, based on experimental results for actual images. The quality of images reconstructed by the proposed method is evaluated by using the relationship between the weighted quantized noise power and their entropy. The proposed method is subjectively evaluated by comparing it with a nonweighted method and the DCT method. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(3): 96–110, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1198

Spatial noise shaping based on human visual sensitivity and its application to image coding

A spatial noise shaping (SNS) method based on human visual sensitivity is presented. The method exploits the capability of frequency domain linear prediction for spatial envelope retrieval. It effectively shapes (or hides) the noise of an image in areas which are not sensitive to human vision so that the resulting image is more pleasant to human eyes. The noise comes from the processing of the image, and it can be either separable like the additive noise pattern in image watermarking or nonseparable like the quantization noise in image coding. An application of the algorithm is demonstrated in the paper by using it to enhance image coders. Images decoded from the SNS incorporated coders have superior perceived quality than those without using SNS.

Differential distributions of red-green and blue-yellow cone opponency across the visual field.

The color vision of Old World primates and humans uses two cone-opponent systems; one differences the outputs of L and M cones forming a red-green (RG) system, and the other differences S cones with a combination of L and M cones forming a blue-yellow (BY) system. In this paper, we show that in human vision these two systems have a differential distribution across the visual field. Cone contrast sensitivities for sine-wave grating stimuli (smoothly enveloped in space and time) were measured for the two color systems (RG & BY) and the achromatic (Ach) system at a range of eccentricities in the nasal field (0-25 deg). We spatially scaled our stimuli independently for each system (RG, BY, & Ach) in order to activate that system optimally at each eccentricity. This controlled for any differential variations in spatial scale with eccentricity and provided a comparison between the three systems under equivalent conditions. We find that while red-green cone opponency has a steep decline away from the fovea, the loss in blue-yellow cone opponency is more gradual, showing a similar loss to that found for achromatic vision. Thus only red-green opponency, and not blue-yellow opponency, can be considered a foveal specialization of primate vision with an overrepresentation at the fovea. In addition, statistical calculations of the level of chance cone opponency in the two systems indicate that selective S cone connections to postreceptoral neurons are essential to maintain peripheral blue-yellow sensitivity in human vision. In the red-green system, an assumption of cone selectivity is not required to account for losses in peripheral sensitivity. Overall, these results provide behavioral evidence for functionally distinct neuro-architectural origins of the two color systems in human vision, supporting recent physiological results in primates.