Perception of Visual Artifacts in Image‐Based Rendering of Façades

Vangorp, Peter; Chaurasia, Gaurav; Laffont, Pierre-Yves; Fleming, Roland W.; Drettakis, George

doi:10.1111/j.1467-8659.2011.01983.x

Cited by 22 publications

(25 citation statements)

References 31 publications

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“…Our work looks to analyze the implicit visual processing behind viewing videos with motion and not static images. Similarly, Vangorp et al [13] conduct psychophysical experiments to understand the perception of artifacts in synthetic rendering. They looked at the user feedback from rendered sequences that moved over facades of buildings.…”

Section: Related Workmentioning

confidence: 99%

“…The advantage of perceptual based graphics has been apparent for a long time, and while a considerable amount of work has been done in measuring conscious, cognitive processing [13,2,1], much less has been done in Computer Graphics to take advantage of covert(brain) measurements and visual processing. In this paper we present a new approach that uses an electroencephalograph (EEG) to interface with the human brain and measure the output of implicit (covert) visual processing in the brain in response to artifacts in image sequences.…”

Section: Introductionmentioning

confidence: 99%

“…During rendering visually objectionable artifacts can arise which limit the application of most rendering algorithms. The most common artifacts that occur in rendered images are ghosting, blurring and popping [13]. In this paper we perform an experiment to analyze the perception of these artifacts.…”

Section: Introductionmentioning

confidence: 99%

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EEG analysis of implicit human visual perception

Mustafa

Lindemann

Magnor

2012

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Image Based Rendering (IBR) allows interactive scene exploration from images alone. However, despite considerable development in the area, one of the main obstacles to better quality and more realistic visualizations is the occurrence of visually disagreeable artifacts. In this paper we present a methodology to map out the perception of IBR-typical artifacts. This work presents an alternative to traditional image and video quality evaluation methods by using an EEG device to determine the implicit visual processes in the human brain. Our work demonstrates the distinct differences in the perception of different types of visual artifacts and the implications of these differences.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EEG analysis of implicit human visual perception

Mustafa

Lindemann

Magnor

2012

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…Despite a substantial amount of research in applying perception to graphics and IBR [Vangorp et al 2011;Ferwerda et al 1996;Anderson et al 2011], only recently has there been a growing interest in using an EEG to analyze visual processing [Mustafa et al 2012;Lindemann and Magnor 2011;Rossion and Caharel 2011;Agam and Sekuler 2007]. Also, the use of an EEG in computer graphics research is now much easier with the advent of systems like the Emotiv EPOC neuroheadset [Emotiv 2012].…”

Section: Introductionmentioning

confidence: 99%

“…According to Vangorp et al [2011] the most common artifacts that occur are ghosting, blurring, and popping. In our test scenes we present videos containing these typical IBR artifacts to participants and then use the recorded EEG to determine the perceived quality.…”

Section: Introductionmentioning

confidence: 99%

Single-trial EEG classification of artifacts in videos

Mustafa

Guthe

Magnor

2012

ACM Trans. Appl. Percept.

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In this article we use an ElectroEncephaloGraph (EEG) to explore the perception of artifacts that typically appear during rendering and determine the perceptual quality of a sequence of images. Although there is an emerging interest in using an EEG for image quality assessment, one of the main impediments to the use of an EEG is the very low Signal-to-Noise Ratio (SNR) which makes it exceedingly difficult to distinguish neural responses from noise. Traditionally, event-related potentials have been used for analysis of EEG data. However, they rely on averaging and so require a large number of participants and trials to get meaningful data. Also, due the the low SNR ERP's are not suited for single-trial classification.We propose a novel wavelet-based approach for evaluating EEG signals which allows us to predict the perceived image quality from only a single trial. Our wavelet-based algorithm is able to filter the EEG data and remove noise, eliminating the need for many participants or many trials. With this approach it is possible to use data from only 10 electrode channels for single-trial classification and predict the presence of an artifact with an accuracy of 85%. We also show that it is possible to differentiate and classify a trial based on the exact type of artifact viewed. Our work is particularly useful for understanding how the human visual system responds to different types of degradations in images and videos. An understanding of the perception of typical image-based rendering artifacts forms the basis for the optimization of rendering and masking algorithms.

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