Fast color correction for multi-view video by modeling spatio-temporal variation

In multi-view video, a number of cameras capture the same scene from different viewpoints. Color variations between the camera views may deteriorate the performance of multi-view video coding or virtual view rendering. In this paper, a fast color correction method for multi-view video is proposed by modeling spatio-temporal variation. In the proposed method, multi-view keyframes are defined to establish the spatio-temporal relationships for accurate and fast implementation. For keyframes, accurate color correction is performed based on spatial color discrepancy model that disparity estimation is used to find correspondence points between views, and linear regression is performed on these sets of points to find the optimal correction coefficients. For non-keyframes, fast color correction is performed based on temporal variations model that time-invariant regions are detected to reflect the change trends of correction coefficients. Experimental results show that compared with other methods, the proposed method can promote the correction speed greatly without noticeable quality degradation, and obtain higher coding performance.     

Enhancing underwater image via color correction and Bi-interval contrast enhancement

Underwater captured images often suffer from color cast and low visibility due to light is scattered and absorbed while it traveling in water. In this paper, we proposed a novel method of color correction and Bi-interval contrast enhancement to improve the quality of underwater images. Firstly, a simple and effective color correction method based on sub-interval linear transformation is employed to address color distortion. Then, a Gaussian low-pass filter is applied to the L channel to decompose the low- and high-frequency components. Finally, the low- and high-frequency components are enhanced by Bi-interval histogram based on optimal equalization threshold strategy and S-shaped function to enhancement image contrast and highlight image details. Inspired by the multi-scale fusion, we employed a simple linear fusion to integrate the enhanced high- and low-frequency components. Comparison with state-of-the-art methods show that the proposed method outputs high-quality underwater images with qualitative and quantitative evaluation well.     

Demosaicing Method for Digital Cameras with White‐RGB Color Filter Array

Demosaicing, or color filter array (CFA) interpolation, estimates missing color channels of raw mosaiced images from a CFA to reproduce full‐color images. It is an essential process for single‐sensor digital cameras with CFAs. In this paper, a new demosaicing method for digital cameras with Bayer‐like W‐RGB CFAs is proposed. To preserve the edge structure when reproducing full‐color images, we propose an edge direction–adaptive method using color difference estimation between different channels, which can be applied to practical digital camera use. To evaluate the performance of the proposed method in terms of CPSNR, FSIM, and S‐CIELAB color distance measures, we perform simulations on sets of mosaiced images captured by an actual prototype digital camera with a Bayer‐like W‐RGB CFA. The simulation results show that the proposed method demosaics better than a conventional one by approximately +22.4% CPSNR, +0.9% FSIM, and +36.7% S‐CIELAB distance.     

Robust Watermarking Scheme Based on Radius Weight Mean and Feature‐Embedding Technique

In this paper, the radius weight mean (RWM) and the feature‐embedding technique are used to present a novel watermarking scheme for color images. Simulations validate that the stego‐images generated by the proposed scheme are robust against most common image‐processing operations, such as compression, color quantization, bit truncation, noise addition, cropping, blurring, mosaicking, zigzagging, inversion, (edge) sharpening, and so on. The proposed method possesses outstanding performance in resisting high compression ratio attacks: JPEG2000 and JPEG. Further, to provide extra hiding storage, a steganographic method using the RWM with the least significant bit substitution technique is suggested. Experiment results indicate that the resulting perceived quality is desirable, whereas the peak signal‐to‐noise ratio is high. The payload generated using the proposed method is also superior to that generated by existing approaches.     

应用于显示器件的动态肤色校正方法

基于输入图像中需要调整的肤色区域动态地移向特征肤色区域的图像动态肤色校正方法,提出了一种适用于显示器件中硬件实现的动态肤色校正简化模型,进而设计实现了实时动态肤色校正电路,并将其应用于100 cm彩色交流等离子体显示器(AC PDP)中进行验证。仿真及实验结果表明,本文提出的实时动态肤色校正方法能够使得校正后的输出图像中皮肤颜色显得更加自然生动,更趋近于人们对肤色的先验理解,从而有效地提高了AC PDP的彩色再现能力。     

基于FPGA的图像自适应AWB系统设计与实现

为了解决常见自动白平衡(Auto White Balance, AWB)方法的场景适应能力不足且实时性较差等问题,提出了一种基于颜色通道直方图重构的自适应AWB方法,并使用现场可编程门阵列(FPGA)对所提出的算法进行硬件电路实现,在校正图像白平衡的同时也确保了系统高速实时处理图像。首先对图像进行限制对比度自适应直方图均衡化(CLAHE)处理来提高图像对比度,然后对图像进行灰度级区间的通道分区统计,对不同场景类别的图像采用颜色直方图匹配或平移的重构方式做自适应处理。实验结果表明,该算法在处理图像白平衡时,相比基于光源估计的AWB算法,色温校正准确率提高了14%,对不同色彩场景有更好的适应性,具有实时处理能力。     

Design and Architecture of Low‐Latency High‐Speed Turbo Decoders

In this paper, we propose and present implementation results of a high‐speed turbo decoding algorithm. The latency caused by (de)interleaving and iterative decoding in a conventional maximum a posteriori turbo decoder can be dramatically reduced with the proposed design. The source of the latency reduction is from the combination of the radix‐4, center to top, parallel decoding, and early‐stop algorithms. This reduced latency enables the use of the turbo decoder as a forward error correction scheme in real‐time wireless communication services. The proposed scheme results in a slight degradation in bit error rate performance for large block sizes because the effective interleaver size in a radix‐4 implementation is reduced to half, relative to the conventional method. To prove the latency reduction, we implemented the proposed scheme on a field‐programmable gate array and compared its decoding speed with that of a conventional decoder. The results show an improvement of at least five fold for a single iteration of turbo decoding.     

Adaptive correction of nonuniform illumination of multispectral digital images

The majority of known methods for correction of multispectral images distorted by nonuniform illumination use the following distortion model: a certain part of the scene close to a directed light source is illuminated much brighter than the rest of the scene. However, another serious problem often arises in practice in the case of nonuniform illumination of 3D objects of the scene: extended heavily shadowed regions with a small area of transition from light to shadow are formed. In this study, a method for the locally adaptive correction of nonuniform illumination of multispectral digital images, which is based on an algorithm that imitates human visual perception, is proposed. The performance of the proposed method is compared to that of available algorithms for correction of color images distorted by both nonuniform illumination and the presence of shadow regions.     

一种基于图像分割的几何变换映射色盲矫正方法

针对基于几何变换映射的色盲矫正方法进行色盲矫正时出现的映射计算量过高的问题,一种新型的几何变换映射色盲矫正方法被提出,这种矫正方法是基于图像分割原理的.首先研究了色盲图像的仿真方法,然后结合K-means和系统聚类算法对原始图像进行分割并计算各个区域在色盲图像下LAB颜色空间中的欧氏距离作为颜色相似性的度量,确定红绿色盲难以分辨的颜色区域.在这个区域内进行颜色映射区域的划分,这个划分主要包括根据领域内的颜色比例对空间内的几何平面进行相应变换,通过此类变换生成有利于色盲患者分辨的图案等步骤.     

Color correction and geometric calibration for multi-view images with feature correspondence

Color and geometry inconsistency between different views is an urgent problem in multi-view imaging applications. In this paper, we present a color correction and geometric calibration method for multi-view images on the basis of feature correspondences between views. First, keypoints in views are detected by using scale invariant feature transform, and accurately matched by bi-directional feature matching between difference views. Then multiplicative and additive errors between matching keypoints are calculated to achieve color correction. In addition, an affine transformation between minimum cost matching keypoints is established to achieve geometric calibration. The experimental results verify the effectiveness of the proposed method in color correction and geometric calibration, and a higher coding efficiency is obtained.     

Object Modeling with Color Arrangement for Region‐Based Tracking

In this paper, we propose a new color histogram model for object tracking. The proposed model incorporates the color arrangement of the target that encodes the relative spatial distribution of the colors inside the object. Using the color arrangement, we can determine which color bin is more reliable for tracking. Based on the proposed color histogram model, we derive a mean shift framework using a modified Bhattacharyya distance. In addition, we present a method of updating an object scale and a target model to cope with changes in the target appearance. Unlike conventional mean shift based methods, our algorithm produces satisfactory results even when the object being tracked shares similar colors with the background.     

A correction method of color projection fringes in 3D contour measurement

In the three-dimensional (3D) contour measurement, the phase shift profilometry (PSP) method is the most widely used one. However, the measurement speed of PSP is very low because of the multiple projections. In order to improve the measurement speed, color grating stripes are used for measurement in this paper. During the measurement, only one color sinusoidal fringe is projected on the measured object. Therefore, the measurement speed is greatly improved. Since there is coupling or interference phenomenon between the adjacent color grating stripes, a color correction method is used to improve the measurement results. A method for correcting nonlinear error of measurement system is proposed in this paper, and the sinusoidal property of acquired image after correction is better than that before correction. Experimental results show that with these correction methods, the measurement errors can be reduced. Therefore, it can support a good foundation for the high-precision 3D reconstruction.     

Supervised‐learning‐based algorithm for color image compression

A correlation exists between luminance samples and chrominance samples of a color image. It is beneficial to exploit such interchannel redundancy for color image compression. We propose an algorithm that predicts chrominance components Cb and Cr from the luminance component Y. The prediction model is trained by supervised learning with Laplacian‐regularized least squares to minimize the total prediction error. Kernel principal component analysis mapping, which reduces computational complexity, is implemented on the same point set at both the encoder and decoder to ensure that predictions are identical at both the ends without signaling extra location information. In addition, chrominance subsampling and entropy coding for model parameters are adopted to further reduce the bit rate. Finally, luminance information and model parameters are stored for image reconstruction. Experimental results show the performance superiority of the proposed algorithm over its predecessor and JPEG, and even over JPEG‐XR. The compensation version with the chrominance difference of the proposed algorithm performs close to and even better than JPEG2000 in some cases.     

Color correction algorithm based on camera characteristics for multi-view video coding

Various types of multi-view camera systems have been proposed for capturing three dimensional scenes. Yet, color distributions among multi-view images remain inconsistent in most cases, degrading multi-view video coding performance. In this paper, we propose a color correction algorithm based on the camera characteristics to effectively solve such a problem. Initially, we model camera characteristics and estimate their coefficients by means of correspondences between views. To consider occlusion in multi-view images, correspondences are extracted via feature-based matching. During coefficient estimation with nonlinear regression, we remove outliers in the extracted correspondences. Consecutively, we generate lookup tables for each camera using the model and estimated coefficients. Such tables are employed for fast color converting in the final color correction process. The experimental results show that our algorithm enhances coding efficiency with gains of up to 0.9 and 0.8 dB for luminance and chrominance components, respectively. Further, the method also improves subjective viewing quality and reduces color distance between views.     

Sliding‐window forward error correction using Reed‐Solomon code and unequal error protection for real‐time streaming video

Forward error correction (FEC) techniques are widely used to recover packet losses over unreliable networks in real‐time video streaming applications. Traditional frame‐level FEC encodes 1 video frame in each FEC coding window. By contrast, in the expanding‐window FEC scheme, high‐priority frames are included in the FEC processing of the following frames, so as to construct a larger coding window. In general, expanding‐window FEC improves the recovery performance of FEC, because the high‐priority frame can be protected by multiple windows and the use of a larger coding window increases the efficiency. However, the larger window size also increases the complexity of the coding and the memory space requirements. Consequently, expanding‐window FEC is limited in terms of practical applications. Sliding‐window FEC adopts a fixed window size in order to approximate the performance of the expanding‐window FEC method, but with a reduced complexity. Previous studies on sliding‐window FEC have generally adopted an equal error protection (EEP) mechanism to simplify the analysis. This paper considers the more practical case of an unequal error protection (UEP) strategy. An analytical model is derived for estimating the playable frame rate (PFR) of the proposed sliding‐window FEC scheme with a Reed‐Solomon erasure code for real‐time non‐scalable streaming applications. The analytical model is used to determine the optimal FEC configuration which maximizes the PFR value under given transmission rate constraints. The simulation results show that the proposed sliding‐window scheme achieves almost the same performance as the expanding‐window scheme, but with a significantly lower computational complexity.     

基于3DGMM的多视点视频直方图颜色校正

多视点视频在采集过程中,由于受到摄像机所处位置以及摄像机自身的参数设置等因素的影响,会造成视点间颜色的不一致,针对这一情况,提出基于三维高斯混合模型的多视点视频直方图颜色校正算法。算法首先获取源图像以及目标图像的直方图并检测直方图的峰值个数,然后结合三维高斯混合模型对源图像以及目标图像进行聚类分析,获取中心矢量,最后计算两图像中像素点距离中心矢量的最小欧几里得距离并分割图像,对源图像和目标图像的匹配区域进行直方图匹配校正。实验结果表明,该算法与传统直方图匹配算法相比,避免了校正过度的影响,校正效果较好。     

TOA Estimation Technique for IR‐UWB Based on Homogeneity Test

This paper deals with the estimation of the time of arrival (TOA) of ultra‐wideband signals under IEEE 802.15.4a channel models. The proposed approach is based on a randomness test and consists of determining whether an autoregressive (AR) process modeling an energy frame is random or not by using a distance to measure the randomness. The proposed method uses a threshold that is derived analytically according to a preset false alarm probability. To highlight the effectiveness of the developed approach, simulation setups as well as real data experiments are conducted to assess the performance of the new TOA estimation algorithm. Thereby, the proposed method is compared with the cell averaging constant false alarm rate technique, the threshold comparison algorithm, and the technique based on maximum energy selection with search back. The obtained results are promising, considering both simulations and collected real‐life data.     

Color calibration of multi-view video plus depth for advanced 3D video

Multi-view video plus depth (MVD) format is considered as the next-generation standard for advanced 3D video systems. MVD consists of multiple color videos with a depth value associated with each texture pixel. Relying on this representation and by using depth-image-based rendering techniques, new viewpoints for multi-view video applications can be generated. However, since MVD is captured from different viewing angles with different cameras, significant illumination and color differences can be observed between views. These color mismatches degrade the performance of view rendering algorithms by introducing visible artifacts leading to a reduced view synthesis quality. To cope with this issue, we propose an effective method for correcting color inconsistencies in MVD. Firstly, to avoid occlusion problems and allow performing correction in the most accurate way, we consider only the overlapping region when calculating the color mapping function. These common regions are determined using a reliable feature matching technique. Also, to maintain the temporal coherence, correction is applied on a temporal sliding window. Experimental results show that the proposed method reduces the color difference between views and improves view rendering process providing high-quality results.     

A Memory‐Efficient Block‐wise MAP Decoder Architecture

Next generation mobile communication system, such as IMT‐2000, adopts Turbo codes due to their powerful error correction capability. This paper presents a block‐wise maximum a posteriori (MAP) Turbo decoding structure with a low memory requirement. During this research, it has been observed that the training size and block size determine the amount of required memory and bit‐error rate (BER) performance of the block‐wise MAP decoder, and that comparable BER performance can be obtained with much shorter blocks when the training size is sufficient. Based on this observation, a new decoding structure is proposed and presented in this paper. The proposed block‐wise decoder employs a decoding scheme for reducing the memory requirement by setting the training size to be N times the block size. The memory requirement for storing the branch and state metrics can be reduced 30% to 45%, and synthesis results show that the overall memory area can be reduced by 5.27% to 7.29%, when compared to previous MAP decoders. The decoder throughput can be maintained in the proposed scheme without degrading the BER performance.