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.

Deep Nets: What have They Ever Done for Vision?

Abstract: This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial intelligence and are of growing importance in cognitive science and neuroscience. They have had many successes but also have several limitations and there is limited understanding of their inner workings. At present Deep Nets perform very well on specific visual tasks with benchmark datasets but they are much less general purpose, flexible, and adaptive than the human visual system. We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. We argue that this combinatorial explosion takes us into a regime where “big data is not enough” and where we need to rethink our methods for benchmarking performance and evaluating vision algorithms. We stress that, as vision algorithms are increasingly used in real world applications, that performance evaluation is not merely an academic exercise but has important consequences in the real world. It is impractical to review the entire Deep Net literature so we restrict ourselves to a limited range of topics and references which are intended as entry points into the literature. The views expressed in this paper are our own and do not necessarily represent those of anybody else in the computer vision community.

Three Dimensional Scalable Video Adaptation via User-End Perceptual Quality Assessment

Abstract: For wireless video streaming, the three dimensional scalabilities (spatial, temporal and SNR) provided by the advanced scalable video coding (SVC) technique can be directly utilized to adapt video streams to dynamic wireless network conditions and heterogeneous wireless devices. However, the question is how to optimally trade off among the three dimensional scalabilities so as to maximize the perceived video quality, given the available resource. In this paper, we propose a low-complexity algorithm that executes at resource-limited user end to quantitatively and perceptually assess video quality under different spatial, temporal and SNR combinations. Based on the video quality measures, we further propose an efficient adaptation algorithm, which dynamically adapts scalable video to a suitable three dimension combination. Experimental results demonstrate the effectiveness of our proposed perceptual video adaptation framework.

Dual-Stream Interactive Networks for No-Reference Stereoscopic Image Quality Assessment

Abstract: The goal of objective stereoscopic image quality assessment (SIQA) is to predict the human perceptual quality of stereoscopic/3D images automatically and accurately. Compared with traditional 2D image quality assessment, the quality assessment of stereoscopic images is more challenging because of complex binocular vision mechanisms and multiple quality dimensions. In this paper, inspired by the hierarchical dual-stream interactive nature of the human visual system, we propose a stereoscopic image quality assessment network (StereoQA-Net) for no-reference stereoscopic image quality assessment. The proposed StereoQA-Net is an end-to-end dual-stream interactive network containing left and right view sub-networks, where the interaction of the two sub-networks exists in multiple layers. We evaluate our method on the LIVE stereoscopic image quality databases. The experimental results show that our proposed StereoQA-Net outperforms state-of-the-art algorithms on both symmetrically and asymmetrically distorted stereoscopic image pairs of various distortion types. In a more general case, the proposed StereoQA-Net can effectively predict the perceptual quality of local regions. In addition, cross-dataset experiments also demonstrate the generalization ability of our algorithm.