Understanding Of Human Visual System Research Articles

Blind image quality assessment by pairwise ranking image series

Image quality assessment (IQA) is constantly innovating, but there are still three types of stickers that have not been resolved: the "content sticker" - limitation of training set, the "annotation sticker" - subjective instability in opinion scores and the "distortion sticker" - disordered distortion settings. In this paper, a No-Reference Image Quality Assessment (NR IQA) approach is proposed to deal with the problems. For "content sticker", we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network; For "annotation sticker", the absolute noise-containing subjective scores are transformed into ranking comparison results, and we design an indirect unsupervised regression based on Eigenvalue Decomposition (EVD); For "distortion sticker", we propose a perception-based distortion classification method, which makes the distortion types clear and refined. Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability. Furthermore, the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.

Quantitative characterization of the human retinotopic map based on quasiconformal mapping.

The retinotopic map depicts the cortical neurons' response to visual stimuli on the retina and has contributed significantly to our understanding of human visual system. Although recent advances in high field functional magnetic resonance imaging (fMRI) have made it possible to generate the in vivo retinotopic map with great detail, quantifying the map remains challenging. Existing quantification methods do not preserve surface topology and often introduce large geometric distortions to the map. In this study, we developed a new framework based on computational conformal geometry and quasiconformal Teichmüller theory to quantify the retinotopic map. Specifically, we introduced a general pipeline, consisting of cortical surface conformal parameterization, surface-spline-based cortical activation signal smoothing, and vertex-wise Beltrami coefficient-based map description. After correcting most of the violations of the topological conditions, the result was a "Beltrami coefficient map" (BCM) that rigorously and completely characterizes the retinotopic map by quantifying the local quasiconformal mapping distortion at each visual field location. The BCM provided topological and fully reconstructable retinotopic maps. We successfully applied the new framework to analyze the V1 retinotopic maps from the Human Connectome Project (n=181), the largest state of the art retinotopy dataset currently available. With unprecedented precision, we found that the V1 retinotopic map was quasiconformal and the local mapping distortions were similar across observers. The new framework can be applied to other visual areas and retinotopic maps of individuals with and without eye diseases, and improve our understanding of visual cortical organization in normal and clinical populations.

Quantitative Characterization of the Human Retinotopic Map Based on Quasiconformal Mapping

Abstract The retinotopic map depicts the cortical neurons’ response to visual stimuli on the retina and has contributed significantly to our understanding of human visual system. Although recent advances in high field functional magnetic resonance imaging (fMRI) have made it possible to generate the in vivo retinotopic map with great detail, quantifying the map remains challenging. Existing quantification methods do not preserve surface topology and often introduce large geometric distortions to the map. In this study, we developed a new framework based on computational conformal geometry and quasiconformal Teichmuller theory to quantify the retinotopic map. Specifically, we introduced a general pipeline, consisting of cortical surface conformal parameterization, surface-spline-based cortical activation signal smoothing, and vertex-wise Beltrami coefficient-based map description. After correcting most of the violations of the topological conditions, the result was a “Beltrami coefficient map” (BCM) that rigorously and completely characterizes the retinotopic map by quantifying the local quasiconformal mapping distortion at each visual field location. The BCM provided topological and fully reconstructable retinotopic maps. We successfully applied the new framework to analyze the V1 retinotopic maps from the Human Connectome Project (n=181), the largest state of the art retinotopy dataset currently available. With unprecedented precision, we found that the V1 retinotopic map was quasiconformal and the local mapping distortions were similar across observers. The new framework can be applied to other visual areas and retinotopic maps of individuals with and without eye diseases, and improve our understanding of visual cortical organization in normal and clinical populations.

Modeling the Perceptual Quality of Stereoscopic Images in the Primary Visual Cortex

Quality assessment of 3-D images encounters more challenges in better understanding of human visual system. In this paper, we propose a perceptual quality assessment approach for stereoscopic images by modeling visual properties of the primary visual cortex. For this purpose, we obtain a new feature encoding approach for the visual information, and define a new similarity measure approach to match the feature encoding to give more reliable and accurate quality assessment. Experimental results on three symmetrically, asymmetrically, and multiple distorted stereoscopic image quality assessment databases demonstrate that our method has high consistency with subjective assessment.