High Dynamic Range Visual Quality of Experience Measurement: Challenges and Perspectives
01 Jan 2015-pp 129-155
TL;DR: The emergence of HDR is seen as an important step towards improving the visual quality of experience (QoE) of the end users, but it comes with its own set of challenges including capture, storage, processing, display, and so on.
Abstract: Traditional capture and display devices can only support a limited dynamic range (contrast) and color gamut given the hardware limitations. As a result, the real physical luminance present in a natural scene cannot be captured by these. However, with the recent advancements in the related software and hardware technologies, it is now possible to capture or reproduce higher contrast and luminance ranges. Such scene-referred visual signals are known as high dynamic range (HDR) signals. They are visually more appealing because they can represent the dynamic range of the visual stimuli present in the real world more accurately. Not surprisingly, the emergence of HDR is seen as an important step towards improving the visual quality of experience (QoE) of the end users. However, HDR comes with its own set of challenges including capture, storage, processing, display, and so on. This chapter focuses on some of those issues from a QoE viewpoint.
Citations
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TL;DR: An objective HDR video quality measure (HDR-VQM) based on signal pre-processing, transformation, and subsequent frequency based decomposition is presented, which is one of the first objective method for high dynamic range video quality estimation.
Abstract: High dynamic range (HDR) signals fundamentally differ from the traditional low dynamic range (LDR) ones in that pixels are related (proportional) to the physical luminance in the scene (i.e. scene-referred). For that reason, the existing LDR video quality measurement methods may not be directly used for assessing quality in HDR videos. To address that, we present an objective HDR video quality measure (HDR-VQM) based on signal pre-processing, transformation, and subsequent frequency based decomposition. Video quality is then computed based on a spatio-temporal analysis that relates to human eye fixation behavior during video viewing. Consequently, the proposed method does not involve expensive computations related to explicit motion analysis in the HDR video signal, and is therefore computationally tractable. We also verified its prediction performance on a comprehensive, in-house subjective HDR video database with 90 sequences, and it was found to be better than some of the existing methods in terms of correlation with subjective scores (for both across sequence and per sequence cases). A software implementation of the proposed scheme is also made publicly available for free download and use. HighlightsThe paper presents one of the first objective method for high dynamic range video quality estimation.It is based on analysis of short term video segments taking into account human viewing behavior.The method described in the paper would be useful in scenarios where HDR video quality needs to be determined in an HDR video chain study.
132 citations
01 Jul 2017
TL;DR: Investigating the relationships between changes in EEG features and subjective quality test scores for High Dynamic Range (HDR) images viewed with a mobile device suggests that increases in the degree of coupling are associated with decreases in HDR quality and suggests that in the HDR image QoE assessment, human emotions play a significant role.
Abstract: Quality of Experience (QoE) assessment of multimedia services is a challenging task and an understanding of how the user perceives quality at the physiological level would facilitate this. Physiological signals, such as the electroencephalogram (EEG), have shown promise in revealing the subject's emotion or attention in quality assessment and the correlation of this with media service quality. This paper investigated the relationships between changes in EEG features and subjective quality test scores (i.e. MOS) for High Dynamic Range (HDR) images viewed with a mobile device. Results show that changes in the gamma and beta bands correlated negatively with MOS, whereas positive correlations were observed in the alpha band. Coupling between activities in the delta and beta bands (i.e. positive correlation between power in the fast beta and slow delta frequency bands) is related to anxiety and dissatisfaction. Thus, the results suggest that increases in the degree of coupling are associated with decreases in HDR quality. This also suggests that in the HDR image QoE assessment, human emotions play a significant role. Potentially, these findings may be exploited in objective QoE perception modelling.
5 citations
Cites background from "High Dynamic Range Visual Quality o..."
...perceive quality from visual stimuli as this can be potentially exploited for developing and optimizing images and video processing algorithms [1]....
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23 Jul 2018
TL;DR: This paper investigates the labeling effect on HDR video QoE by carrying out a series of subjective tests where test participants compared unimpaired HDR videos and found that one of the stimuli in the paired comparison was identified as Premium HDR.
Abstract: High Dynamic Range (HDR) visualization is converging towards a point where it becomes the de facto standard for multimedia services. Current and future variations in color bit depth, peak brightness, black level and all other characteristics of display and content ultimately lead to a fine-grained market of end-user products with a multitude of different capabilities and also numerous quality standards. Such identifiers and indicators may influence the Quality of Experience (QoE), as they create expectations and preconceptions regarding visual quality. In fact, sometimes even a single word may fundamentally affect user experience. In this paper, we investigate the labeling effect on HDR video QoE. We carried out a series of subjective tests where test participants compared unimpaired HDR videos. The trigger of the labeling effect was that one of the stimuli in the paired comparison was identified as Premium HDR. In order to gain more insight into the biased perception and experience, subjective assessment was separately performed for the different quality aspects of HDR visualization, such as luminance or color.
2 citations
Cites background from "High Dynamic Range Visual Quality o..."
...[1] [2] [3] address HDR QoE, taking into consideration immersion, the natural feeling of the visualized content, visual attention and many more aspects, while also discussing subjective measurement methodologies....
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22 May 2016
TL;DR: Preliminary results show that small screen displays (SSDs) have an impact on the performance of TMOs compared to computer displays, and the larger the mobile resolution, the better the subjective results.
Abstract: The development of HDR imaging is seen as an important step towards improving the visual quality of experience (QoE) of the end user in many applications. In practice, Tone-mapping operators (TMOs) provide a useful means for converting a high dynamic range (HDR) image to a low dynamic range (LDR) image in order to achieve better visualization on standard displays. Although mobile devices are becoming popular, the techniques for displaying the content of HDR images on the screens of such devices are still in the early stages. While several studies have been conducted to evaluate TMOs on conventional displays, few studies have been carried out to date to evaluate TMOs on small screen displays, such as those used in mobile devices. In this paper we evaluate, using subjective and objective methods, the most popular Tone-mapping-operators in different mobile displays and resolutions under normal viewing conditions for the end-user. Preliminary results show that small screen displays (SSDs) have an impact on the performance of TMOs compared to computer displays. In general, the larger the mobile resolution, the better the subjective results. We also found clear differences between SSDs and LDRs performances. The best TMO for mobile displays is iCAM06 and for computer displays it is Photographic Reproduction.
2 citations
Cites background or methods from "High Dynamic Range Visual Quality o..."
...Moreover, we will study the impact of HDR imaging on human visual system and investigate and quantify how TMOs modify the QoE by using electroencephalography (EEG) responses [20]....
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...However, the Web is now considered as an important platform for uncontrolled QoE assessments with large numbers of participants....
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...QoE driven multimedia systems have increasingly come into focus in both research and industry....
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...In both studies, the Quality of Experience (QoE) of the end-user was not taken into consideration in the experiments....
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...As there is no established standard for evaluating HDR image quality [6][7][10][18], we chose to use three error metrics; Shannon Entropy (E), The Multi-Exposure Peak Signal Noise Ratio (mPSNR) and Visual difference predictor for HDR images HDR-VDP-2....
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01 Mar 2018
TL;DR: In this paper, the authors present a pixel projeto, which realizes a captura of imagens in a different faixa dinâmica of 76 dB.
Abstract: O processamento no plano focal de imageadores permite que a imagem capturada seja processada em paralelo por toda a matrix de pixels, caracteristica que pode aumentar a velocidade de sistemas de visao. Ao fabricar circuitos dentro da matrix de pixels, o tamanho do pixel aumenta e a razao entre area fotossensivel e a area total do pixel diminui, reduzindo a qualidade da imagem. Para utilizar as vantagens do processamento no plano focal e minimizar a reducao da qualidade da imagem, a primeira parte da tese propoe a inclusao de dois transistores no pixel, o que permite que o espaco de escalas da imagem capturada seja gerado. Nos entao avaliamos em quais condicoes o circuito proposto e vantajoso. Nos analisamos o tempo de processamento e o consumo de energia dessa proposta em comparacao com uma solucao digital. Utilizando um conversor de aproximacoes sucessivas com frequencia de 5.6 MHz, a analise proposta mostra que a abordagem no plano focal e 26 vezes mais rapida que o circuito digital com 10 elementos de processamento, e consome 49 vezes menos energia. Outra maneira de utilizar processamento no plano focal consiste em aplica-lo para melhorar a qualidade da imagem, como na captura de imagens em alta faixa dinâmica. Esta tese tambem apresenta o estudo e projeto de um pixel que realiza a captura de imagens em alta faixa dinâmica atraves do ajuste do tempo de integracao de cada pixel utilizando a iluminacao media e o valor do proprio pixel. Esse pixel foi projetado considerando pequenas variacoes estruturais, como diferentes tamanhos do fotodiodo que realiza a captura do valor de iluminacao medio. Simulacoes de esquematico e pos-layout foram realizadas com o pixel projetado utilizando uma imagem com faixa dinâmica de 76 dB, apresentando resultados com detalhes tanto na parte clara como na parte escura da imagem.
1 citations
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TL;DR: A new technique for the display of high-dynamic-range images, which reduces the contrast while preserving detail, is presented, based on a two-scale decomposition of the image into a base layer.
Abstract: We present a new technique for the display of high-dynamic-range images, which reduces the contrast while preserving detail. It is based on a two-scale decomposition of the image into a base layer,...
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TL;DR: The work presented in this paper leverages the time-tested techniques of photographic practice to develop a new tone reproduction operator and uses and extends the techniques developed by Ansel Adams to deal with digital images.
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TL;DR: A new technique for the display of high-dynamic-range images, which reduces the contrast while preserving detail, is presented, based on a two-scale decomposition of the image into a base layer, encoding large-scale variations, and a detail layer.
Abstract: We present a new technique for the display of high-dynamic-range images, which reduces the contrast while preserving detail. It is based on a two-scale decomposition of the image into a base layer, encoding large-scale variations, and a detail layer. Only the base layer has its contrast reduced, thereby preserving detail. The base layer is obtained using an edge-preserving filter called the bilateral filter. This is a non-linear filter, where the weight of each pixel is computed using a Gaussian in the spatial domain multiplied by an influence function in the intensity domain that decreases the weight of pixels with large intensity differences. We express bilateral filtering in the framework of robust statistics and show how it relates to anisotropic diffusion. We then accelerate bilateral filtering by using a piecewise-linear approximation in the intensity domain and appropriate subsampling. This results in a speed-up of two orders of magnitude. The method is fast and requires no parameter setting.
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TL;DR: The results demonstrate that the method is capable of drastic dynamic range compression, while preserving fine details and avoiding common artifacts, such as halos, gradient reversals, or loss of local contrast.
Abstract: We present a new method for rendering high dynamic range images on conventional displays. Our method is conceptually simple, computationally efficient, robust, and easy to use. We manipulate the gradient field of the luminance image by attenuating the magnitudes of large gradients. A new, low dynamic range image is then obtained by solving a Poisson equation on the modified gradient field. Our results demonstrate that the method is capable of drastic dynamic range compression, while preserving fine details and avoiding common artifacts, such as halos, gradient reversals, or loss of local contrast. The method is also able to significantly enhance ordinary images by bringing out detail in dark regions.
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