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.

Human visual system inspired multi-modal medical image fusion framework

Abstract: Multi-modal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging. The main motivation is to capture most relevant information from sources into a single output, which plays an important role in medical diagnosis. In this paper, a novel framework for medical image fusion based on framelet transform is proposed considering the characteristics of human visual system (HVS). The core idea behind the proposed framework is to decompose all source images by the framelet transform. Two different HVS inspired fusion rules are proposed for combining the low- and high-frequency coefficients respectively. The former is based on the visibility measure, and the latter is based on the texture information. Finally, the fused image is constructed by the inverse framelet transform with all composite coefficients. Experimental results highlight the expediency and suitability of the proposed framework. The efficiency of the proposed method is demonstrated by the different experiments on different multi-modal medical images. Further, the enhanced performance of the proposed framework is understood from the comparison with existing algorithms.

Visual distortion gauge based on discrimination of noticeable contrast changes

Abstract: This paper presents a method to discriminate pixel differences according to their impact toward perceived visual quality. Noticeable local contrast changes are formulated firstly since contrast is the basic sensory feature in the human visual system (HVS) perception. The analysis aims at quantifying the actual impact of such changes (further divided into increases and decreases on edges) in different signal contexts. An associated full-reference distortion metric proposed next provides better match with the HVS viewing. Experiments have used two independent visual data sets and the related subjective viewing results, and demonstrated the performance improvement of the proposed metric over the relevant existing ones with various video/images and under diversified test conditions. The proposed metric is particularly effective to visual signal with blurring and luminance fluctuations as the major artifacts, and brings about the fundamental improvement when sharpened image edges are involved.

DeepFovea: neural reconstruction for foveated rendering and video compression using learned statistics of natural videos

Abstract: In order to provide an immersive visual experience, modern displays require head mounting, high image resolution, low latency, as well as high refresh rate. This poses a challenging computational problem. On the other hand, the human visual system can consume only a tiny fraction of this video stream due to the drastic acuity loss in the peripheral vision. Foveated rendering and compression can save computations by reducing the image quality in the peripheral vision. However, this can cause noticeable artifacts in the periphery, or, if done conservatively, would provide only modest savings. In this work, we explore a novel foveated reconstruction method that employs the recent advances in generative adversarial neural networks. We reconstruct a plausible peripheral video from a small fraction of pixels provided every frame. The reconstruction is done by finding the closest matching video to this sparse input stream of pixels on the learned manifold of natural videos. Our method is more efficient than the state-of-the-art foveated rendering, while providing the visual experience with no noticeable quality degradation. We conducted a user study to validate our reconstruction method and compare it against existing foveated rendering and video compression techniques. Our method is fast enough to drive gaze-contingent head-mounted displays in real time on modern hardware. We plan to publish the trained network to establish a new quality bar for foveated rendering and compression as well as encourage follow-up research.

Perceptual watermarking of still images

Abstract: Content providers on the Internet are faced with the problem of how to secure electronic data. This problem has generated research activity in the area of digital watermarking of electronic content. The challenge is to introduce a digital watermark that is both transparent and highly robust to common signal processing and possible attacks. The two basic requirements for an effective watermarking scheme, robustness and transparency, conflict with each other. We propose a watermarking technique for digital images that is based on utilizing visual models which have been developed in the context of image compression. The visual models give us a direct way to determine the maximum amount of watermark signal that each portion of an image can tolerate without affecting the visual quality of the image. This allows us to provide the maximum strength watermark which in turn, is extremely robust to common image processing and editing such as JPEG compression, rescaling, and cropping. Our watermarking scheme is based on a DCT framework which allows for the possibility of directly watermarking the JPEG bitstream. Our scheme is shown to provide very good results both in terms of image transparency and robustness.

Method of interfacing on a computer network by visual representation of users, method of interacting and computer network

Abstract: A method of interfacing used on a network having a central computer system and a plurality of remote computer system is provided. Each remote computer system includes a video display. The method includes the steps of creating a first visual representation of a first user on the visual display of the first computer system and a second visual representation of a second user on the visual display of the second computer system. The second visual representation is then displayed on the visual display of the first computer system and the first visual representation is displayed on the video display of the second computer system. Applied to video games, the method creates a first visual representation of a first player on a first remote computer system, identifies an interest and a skill level of the first player for at least one video game, indicates predetermined personal characteristics of the first player, saves the visual representation, interest, skill levels and personal characteristics of the first player, accesses the central computer system from the first remote computer system over telephone lines, selects a second player who has accessed the central computer system from a second remote computer system, and invites the second player to play a selected video game. The step of inviting allows the second player to access the visual representation, interest, skill levels and personal characteristics of the first player. The method of interacting is used on a network having a central computer system and a plurality of remote computer systems. Each remote computer system is operated by a user and has access to at least one predetermined application program. The method allows an application program to be employed by at least two primary users and then allows a different user to watch the action of the predetermined application program as it is employed by the primary users. The computer network includes a central computer system, a plurality of remote computer systems connected to the central computer system over telephone lines, means for creating visual representations of users on the visual displays of the remote computer systems, means for sending the visual representation of a user from one remote computer system to a predetermined number of other remote computer systems, and means for running an application program between users of different remote computer systems.