H.264/AVC backward compatible bit-depth scalable video coding

Rate-distortion optimized layered coding of high dynamic range videos

High dynamic range (HDR) video compression has until now been approached by using the high profile of existing state-of-the-art H.264/AVC (Advanced Video Coding) codec or by separately encoding low dynamic range (LDR) video and the residue resulted from the estimation of HDR video from LDR video. Although the latter approach has a distinctive advantage of providing backward compatibility to 8-bit LDR displays, the superiority of one approach to the other in terms of the rate distortion trade-off has not been verified yet. In this paper, we first give a detailed overview of the methods in these two approaches. Then, we experimentally compare two approaches with respect to different objective and perceptual metrics, such as HDR mean square error (HDR MSE), perceptually uniform peak signal to noise ratio (PU PSNR) and HDR visible difference predictor (HDR VDP). We first conclude that the optimized methods for backward compatibility to 8-bit LDR displays are superior to the method designed for high profile encoder both for 8-bit and 12-bit mappings in terms of all metrics. Second, using higher bit-depths with a high profile encoder is giving better rate-distortion performances than employing an 8-bit mapping with an 8-bit encoder for the same method, in particular when the dynamic range of the video sequence is high. Third, rather than encoding of the residue signal in backward compatible methods, changing the quantization step size of the LDR layer encoder would be sufficient to achieve a required quality. In other words, the quality of tone mapping is more important than residue encoding for the performance of HDR image and video coding.

HDR video quality assessment: Perceptual evaluation of compressed HDR video

A gradient domain tone mapping algorithm is proposed to display high dynamic range (HDR) video sequences in low dynamic range (LDR) devices in this work. The proposed algorithm obtains a pixelwise motion vector field and incorporates the motion information into the Poisson equation. Then, by attenuating large spatial gradients, the proposed algorithm can yield a high-quality tone-mapped result without flickering artifacts. Simulation results show that the proposed algorithm provides a better performance than the frame-based method, which processes each frame independently.

The new generation of organic light emitting diode display is designed to enable the high dynamic range (HDR), going beyond the standard dynamic range (SDR) supported by the traditional display devices. However, a large quantity of videos are still of SDR format. Further, most pre-existing videos are compressed at varying degrees for minimizing the storage and traffic flow demands. To enable movie-going experience on new generation devices, converting the compressed SDR videos to the HDR format (i.e., compressed-SDR to HDR conversion) is in great demands. The key challenge with this new problem is how to solve the intrinsic many-to-many mapping issue. However, without constraining the solution space or simply imitating the inverse camera imaging pipeline in stages, existing SDR-to-HDR methods can not formulate the HDR video generation process explicitly. Besides, they ignore the fact that videos are often compressed. To address these challenges, in this work we propose a novel imaging knowledge-inspired parallel networks (termed as KPNet) for compressed-SDR to HDR (CSDR-to-HDR) video reconstruction. KPNet has two key designs: Knowledge-Inspired Block (KIB) and Information Fusion Module (IFM). Concretely, mathematically formulated using some priors with compressed videos, our conversion from a CSDR-to-HDR video reconstruction is conceptually divided into four synergistic parts: reducing compression artifacts, recovering missing details, adjusting imaging parameters, and reducing image noise. We approximate this process by a compact KIB. To capture richer details, we learn HDR representations with a set of KIBs connected in parallel and fused with the IFM. Extensive evaluations show that our KPNet achieves superior performance over the state-of-the-art methods.

Currently, high dynamic range (HDR) videos with high resolution (HR) have become popular due to the display and the rendered technological advancements. However, making ultra-high definition (UHD) with HDR videos is expensive. The legacy low-resolution (LR) standard dynamic range (SDR) format is still largely used in practice. It is necessary to search for a solution to transform LR SDR videos into UHD HDR format. In this paper, we consider joint super resolution and learning inverse tone mapping an issue of high-frequency reconstruction and local contrast enhancement, and we propose an architecture based on a generative adversarial network to apply joint SR-ITM learning. Specifically, we include the residual ResNeXt block (RRXB) as a basic module to better capture high-frequency textures and adopt YUV interpolation to achieve local contrast enhancement. By adopting a generative adversarial network as a pivotal training mechanism, our designs show advantages in both integration and performance. Our code is now available on GitHub: SR-ITM-GAN.

New generations of display technologies provide signicantly improved dynamic range over conventional display devices. Current inverse tone mapping schemes require multiple exposure low dynamic range (LDR) images to generate high dynamic range (HDR) images. In this work, we propose an exposure dependent S curve to convert one optimized LDR image to multiple images with different brightness, which are then fused into a virtual real scene HDR image with wide dynamic range. According to our implementation results, the dynamic range can reach about 105. The synthesizer is robust and temporally coherent, and does not require image specific parameter adjustment. This paper also presents the image quality assessment with HDR visual difference predictor (HDR-VDP) and relative entropy contrast, and our work has better performance than other inverse tone mapping operators (iTMOs) for the both image quality assessments.

High Dynamic Range (HDR) image and video technology aims at conveying the full range of perceptible shadow and highlight details with sufficient precision. HDR is regarded by many experts as the next evolution in digital media. However, industrial broadcasters have concerns regarding the bandwidth overhead that this new technology entails. While many consider that broadcasting HDR content would increase bandwidth requirements by around 20%, this number is based on studies where, in addition to the SDR main stream, HDR-related side information is conveyed. A recent subjective evaluation reported that encoding HDR video content in a single layer might require less bandwidth than its associated SDR version. Similar results were discussed in the MPEG ad-hoc group on High Dynamic Range and Wide Color Gamut. In this article, we explain how having more information can result in lower bandwidth requirements. To this end, we describe several limitations of the human vision system that, when exploited, optimize the HDR distribution pipeline for a human observer. Our theoretical assumption about the higher efficiency of HDR is backed up by a statistical analysis of pixel distribution in real images. The Spatial Index objective metric also reconfirms our assumption.

High dynamic range (HDR) images are becoming pervasive due to capturing or rendering of a wider range of luminance, but their special display equipment is difficult to be popularized because of high cost and technological problem. Thus, HDR images must be adapted to the conventional display devices by applying tone mapping (TM) operation, which puts forward higher requirements for intellectual property protection of HDR images. As the robustness presents regional diversity in the low dynamic range (LDR) watermarked image after TM, which is different from the traditional watermarking technologies, a concept of watermarking activity is defined and used to distinguish the essential distinction of watermarking between LDR image and HDR image in this paper. Then, a novel robust HDR image watermarking algorithm is proposed against TM operations. Firstly, based on the hybrid processing of redundant discrete wavelet transform and singular value decomposition, the watermark is embedded by modifying the structure information of the HDR image. Distinguished from LDR image watermarking, the high embedding strength can cause more obvious distortion in the high brightness regions of HDR image than the low brightness regions. Thus, a perceptual brightness mask with low complexity is designed to improve the imperceptibility further. Experimental results show that the proposed algorithm is robust to the existing TM operations, with taking into account the imperceptibility and embedded capacity, which is superior to the current state-of-art HDR image watermarking algorithms.

In the absence of a commercial High Dynamic Range (HDR) distribution pipeline, two-layer backward-compatible HDR video coding is a viable solution for the imminent transition from Low Dynamic Range (LDR) to HDR content transmission. However, the performance of a two-layer coding solution is governed by the extension layer coding performance. In this paper, we propose an improved two-layer backward-compatible HDR video coding solution based on an adaptive residual mapping for the extension layer, keeping in view the performance of High Efficiency Video Coding (HEVC) being used to code this information. The proposed solution outperforms the reference method achieving averaged PU-PSNR improvements of up to 5.05 dB. The proposed method also shows potential of achieving the same HDR quality as the single layer coding solution with a minimum bitrate overhead and acceptable LDR quality in the base layer.

High dynamic range (HDR) image generation had been studied for years. Due to the expense and rareness of HDR cameras, many works try to generate HDR images using several low dynamic range (LDR) images with different exposure conditions. To ensure high-quality HDR image generation, details of the scene should be retained in different LDR images and the exposure parameters of LDR image should be chosen carefully. In this paper, we propose a technique to dynamically determine the suitable exposure parameter for LDR images according to the property of each scene to be captured. Experimental results reveal that better HDR images are always generated with the LDR images captured by the determined exposure, compared to those created by the method with fixed exposures.

In this paper, we propose a bit-depth scalable lossless coding method for high dynamic range (HDR) images based on a reversible logarithmic mapping. HDR images are generally expressed as floating-point data, such as in the OpenEXR or RGBE formats. Our bit-depth scalable coding approach outputs base layer data and enhancement layer data. It can reconstruct the low dynamic range (LDR) image from the base layer data and reconstructs the HDR image by adding the enhancement layer data. Most previous two-layer methods have focused on the lossy coding of HDR images. Unfortunately, the extension of previous lossy methods to lossless coding does not significantly compress the enhancement layer data. This is because the bit depth becomes very large, especially for HDR images in floating-point data format. To tackle this problem, we apply a reversible logarithmic mapping to the input HDR data. Moreover, we introduce a format conversion to avoid any degradation in the quality of the reconstructed LDR image. The proposed method is effective for both OpenEXR and RGBE formats. Through a series of experiments, we confirm that the proposed method decreases the volume of compressed data while maintaining the visual quality of the reconstructed LDR images.

Due to the growing popularity of High Dynamic Range (HDR) images and HDR displays, a large amount of existing Low Dynamic Range (LDR) images are required to be converted to HDR format to benefit HDR advantages, which give rise to some LDR to HDR algorithms. Most of these algorithms especially tackle overexposed areas during expanding, which is the potential to make the image quality worse than that before processing and introduces artifacts. To dispel these problems, we . present a new,LDR to HDR approach, unlike the existing techniques, it focuses on avoiding sophisticated treatment to overexposed areas in dynamic range expansion step. Based on a separating principle, firstly, according to the familiar types of overexposure, the overexposed areas are classified into two categories which are removed and corrected respectively by two kinds of techniques. Secondly, for maintaining color consistency, color recovery is carried out to the preprocessed images. Finally, the LDR image is expanded to HDR. Experiments show that the proposed approach performs well and produced images become more favorable and suitable for applications. The image quality metric also illustrates that we can reveal more details without causing artifacts introduced by other algorithms.

High dynamic range (HDR) images are highly desirable and widely applied in our daily life. However, most conventional digital cameras are only able to capture low dynamic range (LDR) images. To produce HDR images, a popular and effective method is to merge multiple LDR images with different exposure settings. To achieve high quality HDR images, determining the exposure settings when capturing the multiple LDR images is of great importance. Though many researchers have paid attention to the exposure determination issue for HDR imaging, most of them have focused on DSLR cameras and surveillance video cameras. Meanwhile, mobile devices nowadays have increasing use but few studies in literature focus on them. Since cameras of mobile devices have some features different from DSLR cameras and surveillance video cameras, many existing exposure determination methods may not be directly applied on them. To fill this research gap, this paper proposes a dynamic exposure determination method for HDR photography on mobile devices, in particular on iOS mobile devices. In the proposed method, three LDR images are captured on iOS devices, which are then merged to generate an HDR result image that can cover more details of the target scene. Considering the time and memory limits of mobile devices, the proposed method analyzes the histogram of the target scene thumbnail to determine the three exposure settings with suitable exposure gaps. Based on our proposed method, we have developed an iOS HDR Camera App. The experimental results demonstrate that the HDR result images of this App are capable to contain more details in both bright area and dark area than those of Apple's default HDR Camera App on iPhone 5s for real-world target scenes.

International audience