Response Of Human Visual System Research Articles

Depth Perception Assessment of 3D Videos Based on Stereoscopic and Spatial Orientation Structural Features

Depth quality of stereoscopic three-dimensional (S3D) videos is a significant factor which directly affects the quality of experience (QoE) associated with 3D video applications and services. Nevertheless, there remain limited reports on the investigation of depth perception and depth quality evaluation of S3D videos, which impedes further advancement and deployment of 3D video technology. This paper reports a series of subjective experiments which have been conducted to investigate the depth perception and its related properties of the human visual system (HVS) using S3D video compressed by the H.264/AVC standard. The experimental results reveal that the HVS response in depth perception varies at different frequencies and in varying orientations, and the distortions introduced by video coding can cause the loss of and/or variation in depth perception. By integration of binocular and monocular features (BM) extracted from left and right views of S3D video with respect to depth perception, a depth quality assessment model, herein referred to as BM-DQAM, is devised by training these stereoscopic and spatial orientation structural features with a support vector regression model. It is shown that the BM-DQAM provides a novel no-reference metric for evaluation of the depth quality of S3D videos. Based on two publicly available 3D video databases and the proposed depth perception assessment database, the experimental results show that the BM-DQAM has demonstrated better performance in assessing the depth quality in S3D video viewing than that of other metrics reported in the published literatures, correlating well with the HVS response in the depth perception assessment experiment.

Perceptual Image Hashing With Locality Preserving Projection for Copy Detection

Perceptual image hashing is an effective and efficient way to identify images in large-scale databases, where two major performances are robustness and discrimination. A better tradeoff between robustness and discrimination is still a severe challenge for the current hashing research. Aiming at this issue, we design a novel perceptual image Hashing with Locality Preserving Projection (LPP) (hereafter HLPP). Specifically, to improve the robustness against content-preserving operations, Gabor filtering is leveraged to adaptively extract the orientation and structure features, which are consistent with the response of human visual system. The LPP is adopted to learn intrinsic local structure from the maximum Gabor filtering response. The use of LPP can discover meaningful low-dimensional information hidden in the maximum Gabor filtering response and thus improves discrimination of HLPP. During hash similarity calculation, the Hamming distance is selected as the metric. The tradeoff performance between robustness and discrimination is validated on benchmark databases, and the results indicate that the proposed HLPP is superior to some state-of-the-art algorithms. In addition, extensive experiments of copy detection also demonstrate that the proposed HLPP can provide higher accuracy than the compared algorithms.

PRAM: Penalized Resource Allocation Method for Video Services

Abstract The human visual system response to picture quality degradation due to packet loss is very different from the responses of objective quality measures. While video quality due to packet loss may be impaired by at most for one Group of Pictures (GOP), its subjective quality degradation may last for several GOPs. This has a great impact on resource allocation strategies, which normally make decisions on instantaneous conditions of multiplexing buffer. This is because, when the perceptual impact of degraded video quality is much longer than its objective degradation period, any assigned resources to the degraded flow is wasted. This paper, through both simulations and analysis shows that, during resource allocation, if the quality of a video stream is significantly degraded, it is better to penalize this degraded flow from getting its full bandwidth share and instead assign the remaining share to other flows preventing them from undergoing quality degradation.

Perceptual visual security assessment by fusing local and global feature similarity

Selective encryption is an effective technique for multimedia big data encryption. A perceptual metric that takes the human visual system (HVS) into account is necessary for evaluating the visual security degrees of selectively encrypted images. Existing metrics are mainly based on the similarity of structural information which is represented by local spatial contrast features. However, visual security is concerned with not only the distortion of structural information but also the leakage of important visual information. The local features cannot exactly express the leakage of important visual information. This paper presents a perceptual visual security assessment metric by fusing local and global feature similarity. Considering the HVS response, the proposed metric measures in three aspects: the distortion of structural information, the leakage of important visual information, and the changes of frequency components. To measure the distortion of structure information, local pattern similarity is calculated based on the normalized Hamming distance between the local binary pattern (LBP) binary codes. The similarity of the global LBP histogram is computed to evaluate the leakage of important visual information. A lowpass weighted discrete cosine transform (DCT) frequency similarity is presented to detect the changes of various frequency components. Experimental results demonstrate that the proposed metric achieves significantly higher performance and stronger robustness than the state-of-the-art metrics.

Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

High dynamic range (HDR) image and video technology can provide significant picture quality improvement over the standard dynamic range (SDR). However, when HDR content is represented on an SDR display, dynamic range compression may result in image quality deterioration. To address this problem, we propose an optimized human visual system (HVS) response model-based tone-mapping algorithm to preserve the perceptual responses between the HDR image and its tone-mapped image. First, we measure the HVS response differences using a 2D histogram when an HDR image is displayed on an HDR device and when its tone-mapped image is displayed on an SDR device. Then, we formulate an optimization problem to minimize the differences. By efficiently solving the optimization problem, we obtain an optimal tone-mapping curve. Experimental results on actual displays demonstrate that the proposed algorithm provides superior image quality compared with conventional algorithms in terms of both subjective and objective evaluations.

Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment.

In this paper, we propose a novel design of Human Visual System (HVS) response in a convolutional filter form to decompose meaningful features that are closely tied with image sharpness level. No-reference (NR) Image sharpness assessment (ISA) techniques have emerged as the standard of image quality assessment in diverse imaging applications. Despite their high correlation with subjective scoring, they are challenging for practical considerations due to high computational cost and lack of scalability across different image blurs. We bridge this gap by synthesizing the HVS response as a linear combination of Finite Impulse Response (FIR) derivative filters to boost the falloff of high band frequency magnitudes in natural imaging paradigm. The numerical implementation of the HVS filter is carried out with MaxPol filter library that can be arbitrarily set for any differential orders and cutoff frequencies to balance out the estimation of informative features and noise sensitivities. Utilized by HVS filter, we then design an innovative NR-ISA metric called "HVS-MaxPol" that (a) requires minimal computational cost, (b) produce high correlation accuracy with image sharpness level, and (c) scales to assess synthetic and natural image blur. Specifically, the synthetic blur images are constructed by blurring the raw images using Gaussian filter, while natural blur is observed from real-life application such as motion, out-of-focus, luminance contrast, etc. Furthermore, we create a natural benchmark database in digital pathology for validation of image focus quality in whole slide imaging systems called "FocusPath" consisting of 864 blurred images. Thorough experiments are designed to test and validate the efficiency of HVS-MaxPol across different blur databases and state-of-the-art NR-ISA metrics. The experiment result indicates that our metric has the best overall performance with respect to speed, accuracy and scalability.

Perceptually Optimized Enhancement of Contrast and Color in Images

In this paper, we propose perceptually optimized enhancement of contrast and color in images using just-noticeable-difference (JND) transform and color constancy. We adopt JND transform to get JND map that represents the perceptual response of the human visual system (HVS). We utilize color constancy to estimate the light source color and be robust to color bias. First, we use a perceptual generalized equalization model for the optimization of both color and contrast based on color constancy and contrast enhancement, i.e. base image. Second, we generate JND map based on HVS response model from foreground and background luminance, called JND transform. Next, we update the JND map based on Weber’s law to boost perceptual response. Finally, we perform inverse JND transform from the base image and its JND map to produce the enhanced image highly correlated with the human visual perception. Experimental results show that the proposed method achieves good performance in contrast enhancement, color reproduction, and detail enhancement.

Thresholds of Vision of the Human Visual System: Visual Adaptation for Monocular and Binocular Vision

Thresholds of vision under low-light conditions have been studied for determining the human response to light stimuli in different contexts. Blackwell's work on thresholds of vision of the human visual system did not explore some variables whose analysis would help to characterize more deeply the visual system. This paper extends Blackwell's results exploring new dimensions of the human visual system response including chromaticity of the stimuli, mono/binocular vision, and dark adaptation. Tests have been performed on a sample of 45 observers, with a simplified laboratory setup with respect to Blackwell and without specific hardware/software, producing results in terms of visual adaptation, monocular, and binocular vision and aging effects.

A New Image Enhancement Algorithm Using Just Noticeable Difference and Histogram Equalization

One common way to save the battery life of a portable device is to reduce the LCD backlight intensity, but result in poor quality image. The dim backlight image enhanced to bright image especially for the low-luminance image areas. It has been envisioned as the de-facto solution to the quality image costs of electronic devices such as smart phones. The enhancement algorithm proposed in this paper brightness and visibility prediction for dim image. Based on just noticeable difference (JND) theory and the Human Visual System response model, the algorithm effectively enhances the visibility of image details in dark regions without affecting the contrast of bright regions. Apply the just noticeable difference theory and find the histogram bin finding. This shows the bin table. Finally apply the histogram equalization method. The boosting and color restoration are performed. The purpose of using visibility prediction and brightness for dim backlight image without detail loss and color degradation.

An Improved Scheme on Morphological Image Segmentation Using the Gradients

An improved scheme for contour detection with better performance measure has been proposed. It is based on the response of human visual system during visualization of any type of an image. The scheme consisted of two parts namely to find the edge of the image by using the modified mask of Laplacian of Gaussian edge operator and subsequent modulation of the edge by using watershed algorithm. The method has been applied to a digital image and better performance measure of contour detection has been achieved.

P1-32: Response of Human Visual System to Paranormal Stimuli Appearing in Three-Dimensional Display

Three-dimensional (3D) display became one of indispensable features of commercial TVs in recent years. However, the 3D content displayed by 3D display may contain the abrupt change of depth when the scene changes, which might be considered as a paranormal stimulus. Because the human visual system is not accustomed to such paranormal stimuli in natural conditions, they can cause unexpected responses which usually induce discomfort. Following the change of depth expressed by 3D display, the eyeballs rotate to match the convergence to the new 3D image position. The amount of rotation varies according to the initial longitudinal location and depth displacement of 3D image. Because the change of depth is abrupt, there is delay in human visual system following the change and such delay can be a source of discomfort. To guarantee the safety in watching 3D TV, the acceptable level of displacement in the longitudinal direction should be revealed quantitatively. Additionally, the artificially generated scenes also ...

Color error-diffusion halftoning what differentiates it from grayscale error diffusion?

Grayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.

An objective measurement tool for MPEG video quality

A two-stage objective measurement model for MPEG-coded video is proposed. The first stage weights the coded video distortion according to the human visual system's response. It computes the frame-by-frame perceptual impairment in the decoded picture with respect to a reference picture; this includes low-pass spatial filtering, a Sobel operation to derive masking coefficients, and spatial masking on the raw error between reference and compressed pictures. The second stage, a cognitive emulator, provides a simulation of human high-level processing of visual information. This includes the very low temporal response of human viewers to image quality changes, and asymmetric behaviour in respect of picture quality changes from bad to good, and vice versa. With this model, we have been able to mimic quite accurately the temporally varying subjective picture quality of video sequences as recorded by the ITU-R SSCQE method.

Compressing images with wavelet/subband coding

We have developed software modules (both stand-alone and in the biomedical image analysis and display package ANALYZE) that perform wavelet-based compression on both 2-D and 3-D gray scale images. We present examples of such compression on a variety of medical images, and comparisons with JPEG and other compression schemes. We also show examples of the improvements gained by true 3-D compression of a 3-D image (as opposed to 2-D compression of each slice), and discuss issues such as the treatment of edge effects and human visual system response in the context of a wavelet-based approach. Finally, we discuss extensions of the current approach to still more efficient compression schemes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Hexagonal subband image coding with perceptual weighting

A hexagonally sampled image is split into a low passband and nine passbands of one octave width and 60 deg angular orientation. The conditions to be satisfied by the filter banks for perlect reconstruction are presented. The human visual system's response to stimuli at differing spatial frequencies is then employed to shape the coding noise spectrum. Rate is allocated under a frequency-weighted mean-square-error distortion measure. A framework is presented employing either the power spectral density of the image or the variance of the subbands. Both adaptive and nonadaptive entropy coding simulations are carried out under a Laplacian source distribution. Transparent coding results are presented at rates below 1 bit/pixel.

Transform image enhancement

Blockwise transform image enhancement techniques are discussed. Previously, transform image enhancement has usually been based on the discrete Fourier transform (DFT) applied to the whole image. Two major drawbacks with the DFT are high complexity of implementation involving complex multiplications and additions, with intermediate results being complex numbers, and the creation of severe block effects if image enhancement is done blockwise. In addition, the quality of enhancement is not very satisfactory. It is shown that the best transforms for transform image coding, namely, the scrambled real discrete Fourier transform, the discrete cosine transform, and the discrete cosine-III transform, are also the best for image enhancement. Three techniques of enhancement discussed in detail are alpha-rooting, modified unsharp masking, and filtering motivated by the human visual system response (HVS). With proper modifications, it is observed that unsharp masking and HVS-motivated filtering without nonlinearities are basically equivalent. Block effects are completely removed by using an overlap-save technique in addition to the best transform.