Efficient Dataflow Modeling of Peripheral Encoding in the Human Visual System

TL;DR

This paper introduces a new dataflow model for peripheral vision encoding that is more efficient and compact than previous methods, explicitly includes 'end stopped' features, and is validated through texture perception experiments.

Contribution

We propose a novel dataflow computational model for peripheral encoding that improves efficiency, compactness, and explicitly encodes 'end stopped' features, advancing the modeling of human peripheral vision.

Findings

Our model outperforms prior pooling-based methods in efficiency.

The model is more compact than contrast sensitivity-based approaches.

Perception experiments validate the effectiveness of the encoding in texture perception.

Abstract

Computer graphics seeks to deliver compelling images, generated within a computing budget, targeted at a specific display device, and ultimately viewed by an individual user. The foveated nature of human vision offers an opportunity to efficiently allocate computation and compression to appropriate areas of the viewer's visual field, especially with the rise of high resolution and wide field-of-view display devices. However, while the ongoing study of foveal vision is advanced, much less is known about how humans process imagery in the periphery of their vision -- which comprises, at any given moment, the vast majority of the pixels in the image. We advance computational models for peripheral vision aimed toward their eventual use in computer graphics. In particular, we present a dataflow computational model of peripheral encoding that is more efficient than prior pooling - based methods and more compact than contrast sensitivity-based methods. Further, we account for the explicit encoding of "end stopped" features in the image, which was missing from previous methods. Finally, we evaluate our model in the context of perception of textures in the periphery. Our improved peripheral encoding may simplify development and testing of more sophisticated, complete models in more robust and realistic settings relevant to computer graphics.