高动态范围图像色调映射算法研究

高动态范围(High Dynamic Range,HDR)图像依托自身场景性能的优越性,近年来备受瞩目.然而,HDR图像显示设备由于成本和技术问题而难以普及,HDR图像必须经过色调映射(Tone-Mapping,TM)才能由传统显示器生成自适应低动态范围(Low Dynamic Range,LDR)的数据.近年来,TM...     

HDR技术与区域调光算法

高动态范围图像是当前全球显示及电视行业最热门的先进技术,它可以提供更多的动态范围和图像细节。创维研发的高动态范围、高亮度、高对比度的背光控制技术,实现了LED动态多分区光源处理,可以提供更广、更精准、影像动态范围更高的画面处理能力。本文主要介绍了HDR技术和区域调光背光驱动与算法研究。     

基于重映射和曝光融合的HDR成像

传统的高动态范围（HDR）成像方法需采集多幅图像,不仅容易引入视场误差,而且很难适用于新兴的HDR图像采集系统。为此,提出了一种基于重映射和多尺度曝光融合的简单有效的HDR成像方法。该方法只需要采集一组短、长曝光图像,通过人工重映射分别生成一系列多曝光序列;然后根据曝光评价因子求取多曝光序列权重图;接着对权重图进行高斯金字塔分解,并对多曝光序列进行拉普拉斯金字塔分解;最后基于多尺度曝光融合生成一幅细节保持良好的高质量HDR图像。实验结果表明,对比一些主流的算法,所提方法在多个场景测试中性能较好,图像质量在MEF-SSIM评分中得到较大提升。其中,相比基于梯度场的方法提升了13.33%,相比基于深度学习的方法提升了2.62%。所提方法为HDR成像领域提供了新的参考,可潜在地应用于基于CCD/CMOS的成像系统进行高质量HDR图像采集。     

无线视频传输的跨层动态映射算法

在IEEE 802.11e EDCA机制和基于场景的马尔可夫链模型基础上,提出跨层动态映射算法来改善无线视频传输的服务质量（QoS）。根据视频帧的重要性和网络的负荷情况,将来自应用层的视频帧信息动态地映射到MAC层中合适的队列中去,并且采用网络仿真工具NS2进行仿真,来比较IEEE 802.11e EDCA机制以及跨层动态映射算法。仿真结果表明跨层动态映射算法能明显提高传输后的视频质量。     

HDR显示性能测量研究

介绍了国际标准认可的高动态(HDR)曲线(含PQ和HLG曲线)的技术特点和原理,分析为实现HDR显示,电视产品的技术实现.在此基础上,针对性提出HDR显示性能测量的关键项目,详细介绍测试信号、测量方法,分析现有测试结果.重点讨论对比度测量,给出不同测量方法的差异性和意义.     

显示设备环境自适应色调映射算法

环境光对显示和感知的不利影响是提高显示设备画质的重要障碍之一。本研究提出了一种色调映射算法,可以根据环境光的亮度和颜色自适应调整图像的亮度和颜色,实现不同亮度范围内图像的可靠亮度映射和颜色校正。首先,分析环境光对显示设备的影响,建立显示设备模型。接着,利用对比度感知模型作为桥梁,求解最小化对比度感知差异的亮度映射曲线。然后,利用色貌模型CAM16,将原始图像映射到显示设备所处的环境中,并且保持颜色和亮度的感知不变。最后,将亮度映射和颜色修正结合起来,并设计了软截断函数修正过曝问题。实验和仿真结果表明：在不同的环境下,该算法可以修复由于亮度压缩导致的颜色偏离,将颜色的误差降低到传统方法的60%以下,降低对比度感知误差,获得稳定的画面表现。本算法可以满足复杂环境下显示设备的使用需求。     

一种亮度分区和导向滤波相结合的色调映射算法

针对高动态范围（HighDynamicRange,HDR）图像的动态范围与普通显示设备不匹配的问题,本文提出了一种亮度分区和导向滤波相结合的色调映射算法。首先,通过颜色空间转换将输入的HDR图像转换到CIEL^*a^*b^*空间,得到亮度层和色度层图像,接着对亮度层图像进行对数压缩并分区,对压缩后的各分区亮度图像选取合适γ值进行校正。然后,对于现有映射算法过于强调对图像亮度的处理而忽略色度的问题,本文将压缩后的亮度图像作为引导图像对色度层图像进行导向滤波处理。最后,将处理后的亮度层图像和色度层图像融合得到标准动态范围（StandardDynamicRange,SDR）图像。通过对比实验表明,本文算法得到的图像在清晰度、信息熵、方差方面较全局映射法、局部映射法和双边滤波法平均提高了94.03%、30.73%和24.23%,说明本文算法在实现高动态图像亮度压缩的同时,更好地实现了对色度和局部细节的处理。     

基于交叉分解的高动态范围图像色调映射算法

为了提升高动态范围图像的显示效果,提出一种基于交叉分解的高动态范围图像色调映射算法.在分析图像边缘滤波的基础上,给出一种基于交叉分解的图像分解与重构算法,并对重构系数进行分析,再将其应用到色调映射中.首先在色度亮度空间中对色度、亮度信息进行分离.然后对亮度信息分别使用高斯滤波器和双边滤波器进行滤波以构造出一种交叉分解方...     

AV1编码超高清HDR视频的码率控制技术改进研究

围绕AV1编码在超高清高动态范围（High Dynamic Range,HDR）视频传输中的码率控制技术展开,阐述码率控制技术的作用及AV1编码的原理、特点。剖析现有码率控制技术用于AV1编码超高清HDR视频的局限,并提出一系列改进方案,以期提升AV1编码在超高清HDR视频传输中的性能。     

色度亮度协同滤波的色调映射算法

针对高动态范围图像显现质量有待提高的问题,本文在色度亮度颜色空间中提出了一种基于协同滤波的色调映射算法.首先将输入的高动态范围图像利用色度亮度颜色空间来提取亮度信息和色度信息,然后分别将色度信息和亮度信息进行分解和重构.使用双边滤波技术对亮度信息分解得到亮度基本层和亮度细节层;根据亮度信息和色度信息大尺度边缘的一致性,...     

Evaluation of HDR video tone mapping for mobile devices

Tone mapping operators (TMOs) allow the visual appearance of High Dynamic Range (HDR) video to be reproduced on Low Dynamic Range (LDR) displays. While several studies have been made to compare the performance of different TMOs on standard displays, there is only one preliminary study that takes into account the characteristics of Small Screen Devices (SSDs) which are common on mobile platforms that are growing rapidly in popularity. Due to the unique characteristics of mobile devices, the variety of viewing conditions where they are used, and the fact that they are becoming so widespread, it is important to identify what is the best method to deliver HDR video content to these devices.This paper presents an evaluation of HDR video on mobile devices using an HDR display as reference. Six state-of-the-art HDR video tone mappers are evaluated by conducting a psychophysical experiment where participants were asked to rank the tone mappers applied to different HDR video footage. A comparison was made between tone mapped HDR video footage shown on a tablet and an LCD display compared with the same HDR video footage shown simultaneously on an HDR display. This study shows there is a subtle but statistically significant difference in the preference of TMOs between SSD and LDR even though the overall ordering of TMOs is the same across both displays.     

A unified framework for multi-sensor HDR video reconstruction

One of the most successful approaches to modern high quality HDR-video capture is to use camera setups with multiple sensors imaging the scene through a common optical system. However, such systems pose several challenges for HDR reconstruction algorithms. Previous reconstruction techniques have considered debayering, denoising, resampling (alignment) and exposure fusion as separate problems. In contrast, in this paper we present a unifying approach, performing HDR assembly directly from raw sensor data. Our framework includes a camera noise model adapted to HDR video and an algorithm for spatially adaptive HDR reconstruction based on fitting of local polynomial approximations to observed sensor data. The method is easy to implement and allows reconstruction to an arbitrary resolution and output mapping. We present an implementation in CUDA and show real-time performance for an experimental 4 Mpixel multi-sensor HDR video system. We further show that our algorithm has clear advantages over existing methods, both in terms of flexibility and reconstruction quality.     

Fast bilateral filter for HDR imaging

Bilateral filtering is a method often used in image processing applications. It is specifically useful for HDR algorithms. A novel approach to a fast and close approximation of bilateral filtering is presented. The method is designed especially with a focus on HDR image conversion into a normal color space processing. This paper presents the methods itself, describes the sources of acceleration and discusses the results of the method.     

Methods for improving the tone mapping for backward compatible high dynamic range image and video coding

Backward compatibility for high dynamic range image and video compression forms one of the essential requirements in the transition phase from low dynamic range (LDR) displays to high dynamic range (HDR) displays. In a recent work [1], the problems of tone mapping and HDR video coding are originally fused together in the same mathematical framework, and an optimized solution for tone mapping is achieved in terms of the mean square error (MSE) of the logarithm of luminance values. In this paper, we improve this pioneer study in three aspects by considering its three shortcomings. First, the proposed method [1] works over the logarithms of luminance values which are not uniform with respect to Human Visual System (HVS) sensitivity. We propose to use the perceptually uniform luminance values as an alternative for the optimization of tone mapping curve. Second, the proposed method [1] does not take the quality of the resulting tone mapped images into account during the formulation in contrary to the main goal of tone mapping research. We include the LDR image quality as a constraint to the optimization problem and develop a generic methodology to compromise the trade-off between HDR and LDR image qualities for coding. Third, the proposed method [1] simply applies a low-pass filter to the generated tone curves for video frames to avoid flickering during the adaptation of the method to the video. We instead include an HVS based flickering constraint to the optimization and derive a methodology to compromise the trade-off between the rate-distortion performance and flickering distortion. The superiority of the proposed methodologies is verified with experiments on HDR images and video sequences.     

高动态范围数字相机sRGB色彩空间颜色管理

数字成像设备在传输、显示图像时需要进行设备的特性化处理,该过程是设备颜色管理中的重要环节。为了保证输出图像的色彩复现能力,高动态范围数字相机需要根据设备自身特性进行特性化。现有特性化过程一般针对8位数字相机,并且转换矩阵的标定较为复杂,容易产生系统误差,矩阵精度易受影响。针对上述问题,提出了一种高动态范围彩色数字相机颜色管理方法,利用相机输出图像与被摄目标的色彩属性建立了RGB色彩空间到CIE1931 XYZ色彩空间的映射关系,并采用最小二乘拟合法对映射矩阵进行标定,最终将相机的RGB色彩空间转换至s RGB标准色彩空间,解决了高动态相机的特性化问题。实验证明,该方法操作简单,设备通用性较强,拟合均方误差优于0.08,具有较好的鲁棒性。     

Objective and subjective evaluation of High Dynamic Range video compression

A number of High Dynamic Range (HDR) video compression algorithms proposed to date have either been developed in isolation or only-partially compared with each other. Previous evaluations were conducted using quality assessment error metrics, which for the most part were developed for qualitative assessment of Low Dynamic Range (LDR) videos. This paper presents a comprehensive objective and subjective evaluation conducted with six published HDR video compression algorithms. The objective evaluation was undertaken on a large set of 39 HDR video sequences using seven numerical error metrics namely: PSNR, logPSNR, puPSNR, puSSIM, Weber MSE, HDR-VDP and HDR-VQM. The subjective evaluation involved six short-listed sequences and two ranking-based subjective experiments with hidden reference at two different output bitrates with 32 participants each, who were tasked to rank distorted HDR video footage compared to an uncompressed version of the same footage. Results suggest a strong correlation between the objective and subjective evaluation. Also, non-backward compatible compression algorithms appear to perform better at lower output bit rates than backward compatible algorithms across the settings used in this evaluation.     

视频压缩中运动估计块匹配算法研究

首先介绍了视频压缩系统，并在此基础上阐述了运动估计块匹配算法，分别介绍了块匹配的基本原理、提高运动估计效率的几个关键技术，分析了传统的基于块的运动估计块匹配算法和后来改进的匹配算法的性能，同时在分析MPEG-4特性的基础上，阐述了未来的运动估计匹配算法的发展趋势。     

4K超高清节目制播中HDR与SDR的兼容与转换

HDR(High Dynamic Range,高动态范围)既是4K超高清电视的亮点,同时也是难点和痛点。HDR与SDR(Standard Dynamic Range,标准动态范围)在显示亮度、对比度、色域、量化比特数、光电和电光转换函数等方面都不相同,存在兼容难题。本文基于相关测试,研究了HDR和SDR内容在不同显示终端上的呈现差异,以及HDR和SDR节目转换中存在的问题,提出了4K HDR在发展过程中需要关注的相关问题。     

交通视频的移动目标检测算法研究

对视频序列的目标进行识别和跟踪监控是计算机视觉领域的关键技术。依据图像处理和目标检测研究方向的热点算法,将帧间差分与混合高斯背景模型融合法应用于交通视频移动车辆的目标检测分析中。该算法通过背景差分求出光照影响的动态阈值范围,若光照检测阈值小于该动态阈值,选择混合高斯背景模型法,大于该动态阈值则选取帧间差分法。再通过开运算、闭运算、连通域计算,以及区域的凸包形态学运算即可准确描述目标。实验结果表明,该融合算法消除了鬼影现象,在光照环境改善的情况下可以有效应用于交通视频中移动车辆的目标检测。