Foveation Filtering Research Articles

A Retina-Based Perceptually Lossless Limit and a Gaussian Foveation Scheme With Loss Control

In this paper, we propose a new limit that promises theoretically achievable data reduction ratios up to approximately 9:1 with no perceptual loss in typical scenarios. Also, we introduce a novel Gaussian foveation scheme that provides experimentally achievable gains up to approximately 2 times the compression ratio of typical compression schemes with less perceptual loss than in typical transmissions. Both the proposed limit and foveation scheme shares the same background material: a model of image projection onto the retina; a model of cones distribution; and, subsequently, a proposed pointwise retina-based constraint called pixel efficiency. Quantitatively, the lattermost is globally processed to reveal the perceptual efficiency of a display. Analytical results indicate that in general the perceptual efficiency of displays are low for typical image sizes and viewing distances. Qualitatively, the pixel efficiency is used together with a lossy parameter to locally control the spatial resolution of a foveated image. Practical results show that proper use of the lossy parameter in the foveation filtering can increase the subjective quality of images.

Video Processing for Human Perceptual Visual Quality-Oriented Video Coding

We have developed a video processing method that achieves human perceptual visual quality-oriented video coding. The patterns of moving objects are modeled by considering the limited human capacity for spatial-temporal resolution and the visual sensory memory together, and an online moving pattern classifier is devised by using the Hedge algorithm. The moving pattern classifier is embedded in the existing visual saliency with the purpose of providing a human perceptual video quality saliency model. In order to apply the developed saliency model to video coding, the conventional foveation filtering method is extended. The proposed foveation filter can smooth and enhance the video signals locally, in conformance with the developed saliency model, without causing any artifacts. The performance evaluation results confirm that the proposed video processing method shows reliable improvements in the perceptual quality for various sequences and at various bandwidths, compared to existing saliency-based video coding methods.

움직임 분석 기반의 시각인지 모델을 이용한 비디오 코딩 방법

본 논문에서는 인간 인지 기반 비디오 코딩을 위한 비디오 처리 방법을 개발한다. 제안하는 방법은 율-왜곡(rate-distortion) 최적화의 영향뿐만 아니라 제한적인 시, 공간 해상도, 지역적인 움직임 이력(history), visual saliency에 의한 인간 시각 인지를 고려한다. 이러한 인간의 인지적인 효과들을 고려하기 위하여 본 논문에서는 움직임 패턴을 모델링하고 Hedge 알고리듬을 사용하여 움직임 패턴을 결정하는 기법을 개발한다. 그 다음, 제안한 움직임 패턴과 기존의 visual saliency와의 결합을 통하여 인간 시각 인지 모델을 수립한다. 제안된 인간 시각 인지 모델을 구현하기 위하여 기존의 foveation filtering 방법을 확장한다. 시각적 자극이 덜한 지역만을 부드럽게(smoothing)하는 기존의 foveation filtering 기법과 비교하여 제안하는 foveation filtering 기법은 인간 시각 인지 모델에 따라 지역적으로 부드럽게 또는 지역적 특성을 향상시킴으로써, 시각적 자극이 덜한 지역에서 줄여진 대역폭을 효과적으로 시각적 자극이 큰 지역에서 사용하도록 이동 시킬 수 있는 장점이 있다. 제안된 방법의 성능은 전반적인 비디오 화질을 만족할 뿐만 아니라 인간이 인지하는 화질의 품질을 12%~44% 향상시킨다. We develop a video processing method that allows the more advanced human perception oriented video coding. The proposed method necessarily reflects all influences by the rate-distortion based optimization and the human visual perception that is affected by the visual saliency, the limited space-time resolution and the regional moving history. For reflecting the human perceptual effects, we devise an online moving pattern classifier using the Hedge algorithm. Then, we embed the existing visual saliency into the proposed moving patterns so as to establish a human visual perception model. In order to realize the proposed human visual perception model, we extend the conventional foveation filtering method. Compared to the conventional foveation filter only smoothing less stimulus video signals, the developed foveation filter can locally smooth and enhance signals according to the human visual perception without causing any artifacts. Due to signal enhancement, the developed foveation filter more efficiently transfers the bandwidth saved at smoothed signals to the enhanced signals. Performance evaluation verifies that the proposed video processing method satisfies the overall video quality, while improving the perceptual quality by 12%~44%.

Real-time foveation filtering using nonlinear Mipmap interpolation

In recent years, several techniques have been proposed to simulate the gaze effect of the Human Visual System (HVS). It is believed that this effect is due to the foveation filtering. Current techniques to simulate the foveation filtering in computer graphics are either slow or suffer from artifacts and limitations. In this paper, we present a new approach of foveation filtering based on the Mipmap Pyramid of the current view by considering the relationship between the Gaussian kernel and Mipmap level. Due to the nonlinear Mipmap interpolation under the Bilateral Filtering scheme, we are able to simulate the foveation filtering more naturally and efficiently than in previous work. Moreover, a detail enhancement method based on the Cornsweet illusion is proposed to augment the gazing effect. We demonstrate our new approach with a variety of examples and provide comparisons with recent approaches.

Quantitative modelling of perceptual salience at human eye position

We investigate the extent to which a simple model of bottom-up attention and salience may be embedded within a broader computational framework, and compared with human eye movement data. We focus on quantifying whether increased simulation realism significantly affects quantitative measures of how well the model may predict where in video clips humans direct their gaze. We hence compare three variants of the model, tested with 15 video clips of natural scenes shown to three observers. We measure model-predicted salience at the locations gazed to by the observers, compared to random locations. The first variant simply processes the raw video clips. The second adds a gaze-contingent foveation filter. The third further attempts to realistically simulate dynamic human vision by embedding the video frames within a larger background, and shifting them to eye position. Our main finding is that increasing simulation realism significantly improves the predictive ability of the model. Better emulating the details of how a visual stimulus is captured by a constantly rotating retina during active vision has a significant positive impact onto quantitative comparisons between model and human behaviour.

Fast algorithms for foveated video processing

This paper explores the problem of communicating high-quality, foveated video streams in real time. Foveated video exploits the nonuniform resolution of the human visual system by preferentially allocating bits according to the proximity to assumed visual fixation points, thus delivering perceptually high quality at greatly reduced bandwidths. Foveated video streams possess specific data density properties that can be exploited to enhance the efficiency of subsequent video processing. Here, we exploit these properties to construct several efficient foveated video processing algorithms: foveation filtering (local bandwidth reduction), motion estimation, motion compensation, video rate control, and video postprocessing. Our approach leads to enhanced computational efficiency by interpreting nonuniform-density foveated images on the uniform domain and by using a foveation protocol between the encoder and the decoder.