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Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.


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Journal ArticleDOI
TL;DR: The Haar wavelet-based perceptual similarity index (HaarPSI) as discussed by the authors was proposed to assess local similarities between two images, as well as the relative importance of image areas.
Abstract: In most practical situations, the compression or transmission of images and videos creates distortions that will eventually be perceived by a human observer. Vice versa, image and video restoration techniques, such as inpainting or denoising, aim to enhance the quality of experience of human viewers. Correctly assessing the similarity between an image and an undistorted reference image as subjectively experienced by a human viewer can thus lead to significant improvements in any transmission, compression, or restoration system. This paper introduces the Haar wavelet-based perceptual similarity index (HaarPSI), a novel and computationally inexpensive similarity measure for full reference image quality assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. The consistency of the HaarPSI with the human quality of experience was validated on four large benchmark databases containing thousands of differently distorted images. On these databases, the HaarPSI achieves higher correlations with human opinion scores than state-of-the-art full reference similarity measures like the structural similarity index (SSIM), the feature similarity index (FSIM), and the visual saliency-based index (VSI). Along with the simple computational structure and the short execution time, these experimental results suggest a high applicability of the HaarPSI in real world tasks.

193 citations

Journal ArticleDOI
TL;DR: This work investigates the quantification of depth and size perception of virtual objects relative to real objects in combined real and virtual environments, and preliminary experimental results on the perceived depth of spatially nonoverlapping real andvirtual objects are presented.
Abstract: With the rapid advance of real-time computer graphics, head-mounted displays HMDs have become popular tools for 3D visualization. One of the most promising and challenging future uses of HMDs, however, is in applications where virtual environments enhance rather than replace real environments. In such applications, a virtual image is superimposed on a real image. The unique problem raised by this superimposition is the difficulty that the human visual system may have in integrating information from these two environments. As a starting point to studying the problem of information integration in see-through environments, we investigate the quantification of depth and size perception of virtual objects relative to real objects in combined real and virtual environments. This starting point leads directly to the important issue of system calibration, which must be completed before perceived depth and sizes are measured. Finally, preliminary experimental results on the perceived depth of spatially nonoverlapping real and virtual objects are presented.

192 citations

Reference EntryDOI
Frank Tong1
23 Mar 2018

192 citations

Journal ArticleDOI
TL;DR: A new JND estimator for color video is devised in image-domain with the nonlinear additivity model for masking and is incorporated into a motion-compensated residue signal preprocessor for variance reduction toward coding quality enhancement, and both perceptual quality and objective quality are enhanced in coded video at a given bit rate.
Abstract: We present a motion-compensated residue signal preprocessing scheme in video coding scheme based on just-noticeable-distortion (JND) profile Human eyes cannot sense any changes below the JND threshold around a pixel due to their underlying spatial/temporal masking properties An appropriate (even imperfect) JND model can significantly help to improve the performance of video coding algorithms From the viewpoint of signal compression, smaller variance of signal results in less objective distortion of the reconstructed signal for a given bit rate In this paper, a new JND estimator for color video is devised in image-domain with the nonlinear additivity model for masking (NAMM) and is incorporated into a motion-compensated residue signal preprocessor for variance reduction toward coding quality enhancement As the result, both perceptual quality and objective quality are enhanced in coded video at a given bit rate A solution of adaptively determining the parameter for the residue preprocessor is also proposed The devised technique can be applied to any standardized video coding scheme based on motion compensated prediction It provides an extra design option for quality control, besides quantization, in contrast with most of the existing perceptually adaptive schemes which have so far focused on determination of proper quantization steps As an example for demonstration, the proposed scheme has been implemented in the MPEG-2 TM5 coder, and achieved an average peak signal-to-noise (PSNR) increment of 0505 dB over the twenty video sequences which have been tested The perceptual quality improvement has been confirmed by the subjective viewing tests conducted

191 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202349
202294
2021279
2020311
2019351
2018348