Auto White Balance Using the Coincidence of Chromaticity Histograms

Jiang, Tao; Nguyen, Duong-Van; Kuhnert, Klaus-Dieter

doi:10.1109/sitis.2012.68

Cited by 11 publications

(10 citation statements)

References 15 publications

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“…OA is a widely used parameter to evaluate the quality of AWB, the larger the better [16]. The histograms of red, green and blue channels for an image are shown in Fig.…”

Section: B Color Cast For An Imagementioning

confidence: 99%

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A Low Power and Low Complexity Automatic White Balance Algorithm for AMOLED Driving Using Histogram Matching

Huang

Zhang

Wang

et al. 2015

J. Display Technol.

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This paper presents a low power, low complexity and high hardware efficiency automatic white balance algorithm based on histogram matching (AWB-HM) for active-matrix organic light-emitting diode (AMOLED) driving. The main idea of AWB-HM algorithm is to reduce the display power by power constraint, and then increase the overlap area (OA) of three color histograms by histogram matching. The computation complexity in the proposed algorithm is reduced without estimating the gains of red, green and blue channels in the conventional automatic white balance (AWB) algorithms. Moreover, adders rather than multipliers are mainly used in the algorithm, which is easier for the hardware realization. The simulation results show that the value of OA for the images processed by the proposed algorithm is 26.4% and 58.7% larger than that of gray-world (GW) and white-patch (WP) algorithm, respectively. Furthermore, the power dissipation and the time complexity is reduced compared with other algorithms. The proposed algorithm has been applied to AMOLED and the color cast of image is improved significantly.Index Terms-AMOLED, automatic white balance, histogram matching, overlap area. 1551-319X (c)

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“…OA is a widely used parameter to evaluate the quality of AWB, the larger the better [16]. The histograms of red, green and blue channels for an image are shown in Fig.…”

Section: B Color Cast For An Imagementioning

confidence: 99%

“…3(f). As OA is an important parameter for AWB [16], larger value of OA means lower level of color cast. The core idea of this work is to weaken the color cast by increasing the value of OA.…”

Section: The Theory Of the Proposed Algorithmmentioning

confidence: 99%

A Low Power and Low Complexity Automatic White Balance Algorithm for AMOLED Driving Using Histogram Matching

Huang

Zhang

Wang

et al. 2015

J. Display Technol.

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“…In particular, the color of the illuminant has a crucial repercussion on the recorded color of the digital image. Hence, it is significantly meaningful to eliminate undesirable illuminant effects in order to facilitate robust performance of color-based computer vision systems such as human-computer interaction, video analytics, object tracking, color feature extraction and digital photography [5,6]. For the past two decades, computer vision researchers have suggested a lot of color constancy adjustment techniques to cope with coloration and color cast in digital images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Computational Color Constancy With Convoluted Mixture of Deep Experts (CMoDE) Fusion Technique

Choi

Yun

2020

IEEE Access

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In the human and computer vision, color constancy is the ability to perceive the true color of objects in spite of changing illumination conditions. Color constancy is remarkably benefitting human and computer vision issues such as human tracking, object and human detection and scene understanding. Traditional color constancy approaches based on the gray world assumption fall short of performing a universal predictor, but recent color constancy methods have greatly progressed with the introduction of convolutional neural networks (CNNs). Yet, shallow CNN-based methods face learning capability limitations. Accordingly, this article proposes a novel color constancy method that uses a multi-stream deep neural network (MSDNN)-based convoluted mixture of deep experts (CMoDE) fusion technique in performing deep learning and estimating local illumination. In the proposed method, the CMoDE fusion technique is used to extract and learn spatial and spectral features in an image space. As opposed to previous works where residual networks stack multiple layers linearly and concatenate multiple paths, the proposed method distinctively piles up layers both in series and in parallel, selects and concatenates effective paths in the CMoDE-based DCNN. As a result, the proposed CMoDE-based DCNN brings significant progress to efficiency and accuracy of using computing resources and estimating illuminants, respectively. In the experiments, Shi's Reprocessed, gray-ball and NUS-8 Camera datasets are used to prove illumination and camera invariants. The experimental results illustrate that this new method surpasses its conventional counterparts. INDEX TERMS color constancy, CMoDE fusion technique, multi-stream deep neural network (MSDNN), illumination estimation, residual networks.

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“…An image shot captured under a high illuminant colour temperature often appears to have blue colour cast and reddish colour cast if it is taken under low colour temperature. Human visual system has special features that can distinguish the source illuminant temperature, colour, of the object, therefore a scene illuminated by different colour illuminant; look as if they have been taken under canonical colour lights [1] [2]. The ultimate aim of the colour constancy algorithms is to adopt human colour constancy feature into machine vision so that a captured scene under different light sources looks as if it has been taken under canonical light [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Max-RGB Based Colour Constancy Using the Sub-blocks of the Image

Hussain

Sheikh-Akbari

2016

2016 9th International Conference on Developments in eSystems Engineering (DeSE)

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Abstract-Colour constancy refers to the task of revealing the true colour of an object despite ambient presence of intrinsic illuminant. The performance of most of the existing colour constancy algorithms are deteriorated when image contains a big patch of uniform colour. This paper presents a Max-RGB based colour constancy adjustment method using the sub-blocks of the image to significantly reduce the effect of the large uniform colour area of the scene on colour constancy adjustment of the image. The proposed method divides the input image into a number of non-overlapping blocks and computes the Average Absolute Difference (AAD) value of each block's colour component. The blocks with the AADs greater than threshold values are considered having sufficient colour variation to be used for colour constancy adjustment. The Max-RGB algorithm is then applied to the selected blocks' pixels to calculate colour constancy scaling factors for the whole image. Evaluations of the performance of the proposed method on images of three benchmark datasets show that the proposed method outperforms the state of the art techniques in the presence of large uniform colour patches.

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Auto White Balance Using the Coincidence of Chromaticity Histograms

Cited by 11 publications

References 15 publications

A Low Power and Low Complexity Automatic White Balance Algorithm for AMOLED Driving Using Histogram Matching

A Low Power and Low Complexity Automatic White Balance Algorithm for AMOLED Driving Using Histogram Matching

Deep Learning-Based Computational Color Constancy With Convoluted Mixture of Deep Experts (CMoDE) Fusion Technique

Max-RGB Based Colour Constancy Using the Sub-blocks of the Image

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