Image display algorithms for high‐ and low‐dynamic‐range display devices

Reinhard, Erik; Kunkel, Timo; Marion, Yoann; Brouillat, Jonathan; Cozot, Rémi; Bouatouch, Kadi

doi:10.1889/1.2825110

Cited by 21 publications

(12 citation statements)

References 54 publications

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“…Furthermore, it is well known in HDR imaging that a linear TMO often results in a dark image and loss of detail. 32 The fact that we can clearly visualize the Figure 2a and observe little loss of detail also indicates the lack of DR in the content.…”

Section: Subjective Scoresmentioning

confidence: 82%

Measuring quality of omnidirectional high dynamic range content

Perrin

Bist

Cozot

et al. 2017

Applications of Digital Image Processing XL

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Although HDR content processing, coding and quality assessment have been largely addressed in the last few years, little to no work has been concentrating on how to assess quality in HDR for 360• or omnidirectional content. This paper is an attempt to answer to various questions in this direction. As a minimum, a new data set for 360• HDR content is proposed and a new methodology is designed to assess subjective quality of HDR 360• content when it is displayed on SDR HMD after applying various tone mapping operators. The results are then analyzed and conclusions are drawn.

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Section: Subjective Scoresmentioning

confidence: 82%

Measuring quality of omnidirectional high dynamic range content

Perrin

Bist

Cozot

et al. 2017

Applications of Digital Image Processing XL

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“…To compute the luminance image of color images, several methods can be used based on NTSC (National Television System Committee) standard [9], sRGB (standard Red-Green-Blue) standard [15], or the definition of HSV (Hue-Saturation-Value) intensity value [16]. However, according to [10], adopting HSV intensity value is suggested because it achieves color consistency in the RGB color image enhancement without color space conversion.…”

Section: The Existing Sdrclce Algorithmmentioning

confidence: 99%

“…According to [14], the remapping of luminance and chrominance components of each YC b C r color pixel are respectively given by 15) where ρ(x, y) is the color-mapping ratio defined in (8.7) with L in (x, y) = Y in (x, y), and C i j (i = b, r, and j = in, out) denotes one of the chrominance components. Combining the proposed algorithm with the YC b C r linear color restoration formulas (8.14) and (8.15) allows directly enhancing video signals without color space conversion, significantly speeding up the video enhancement process.…”

Section: Extension To Video Signal Processingmentioning

confidence: 99%

“…[15] reported two lookup table (LUT) acceleration methods, which are also applicable to the proposed algorithm. 8.3 Two LUT acceleration methods of the adaptive SDRCLCE algorithm to achieve realtime performance on the CPU: Accelerated implementation a by using a 256-by-256 2D LLUT required updating according to the current value of w, and b by using a N-by-256-by-256 3D LLUT combined with a linear interpolation method uses Eqs.…”

Section: Gpu Acceleration Of the Adaptive Sdrclce Algorithmmentioning

confidence: 99%

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A GPU-Accelerated Adaptive Simultaneous Dynamic Range Compression and Local Contrast Enhancement Algorithm for Real-Time Color Image Enhancement

Tsai

2015

Color Image and Video Enhancement

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Modern robotic vision systems usually require the use of image enhancement algorithms as they play an important preliminary processing role in computer vision applications. Numerous image enhancement methods have been proposed to deal with monochromatic images. However, they often cannot be directly applied on multichannel images, which usually give more information about the environment than monochromatic images. Consequently, color image enhancement has now become an active research issue in image enhancement processing. Moreover, because color images contain much more data to be processed, the improvement on computational efficiency of color image enhancement to achieve real-time performance is still a challenging task.This chapter presents a parallel implementation of an existing adaptive color image enhancement scheme, which improves visual quality of color images suffered from low dynamic range (LDR) and poor contrast defects. Images captured from a digital camera may be in poor visibility due to the limitation on dynamic range of image sensors. This problem typically can be resolved by compressing the dynamic range of captured images, and this image enhancement process is commonly known as dynamic range compression. Several dynamic range compression techniques have been proposed based on Retinex theory, which is a lightness and color perception model of human vision [1]. Retinex-based techniques [2][3][4][5][6][7] are general purpose methods, simultaneously achieving dynamic range compression, local-contrast enhancement, and color consistency; however, these algorithms are usually computationally expensive, requiring algorithmic simplification and hardware acceleration to achieve real-time performance [8].

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“…Generally, algorithms for the dynamic range expansion from an LDR image to an HDR image are called inverse‐tone‐reproduction operators; several have been proposed …”

Section: Introductionmentioning

confidence: 99%

Rendering of HDR images to improve brightness discrimination

Hong

Shin

Song

et al. 2016

Jnl Soc Info Display

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The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.

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Image display algorithms for high‐ and low‐dynamic‐range display devices

Cited by 21 publications

References 54 publications

Measuring quality of omnidirectional high dynamic range content

Measuring quality of omnidirectional high dynamic range content

A GPU-Accelerated Adaptive Simultaneous Dynamic Range Compression and Local Contrast Enhancement Algorithm for Real-Time Color Image Enhancement

Rendering of HDR images to improve brightness discrimination

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