Fully-automatic inverse tone mapping preserving the content creator's artistic intentions

High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show this LDR content on HDR displays, a dynamic range expansion by using an Inverse Tone Mapped Operator (iTMO) is required. In addition to requiring human intervention for tuning, most of the iTMOs don't consider artistic intentions inherent to the HDR domain. Furthermore, the quality of their results decays with peak brightness above 1000 nits. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping. This allows expanding LDR images into HDR with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using full-reference objective quality metrics as HDR-VDP-2.2 and DRIM. Experimental results demonstrate that our proposed method outperforms the current state of the art

Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping

High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show LDR content on HDR displays, it needs to be up-scaled using a so-called inverse tone mapping algorithm. Several techniques for inverse tone mapping have been proposed in the last years, going from simple approaches based on global and local operators to more advanced algorithms such as neural networks. Some of the drawbacks of existing techniques for inverse tone mapping are the need for human intervention, the high computation time for more advanced algorithms, limited low peak brightness, and the lack of the preservation of the artistic intentions. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping capable of real-time video processing. Our proposed algorithm allows expanding LDR images into HDR images with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using the full-reference objective quality metrics HDR-VDP-2.2 and DRIM, and carrying out a subjective pair-wise comparison experiment. We compared our results with those obtained with the most recent methods found in the literature. Experimental results demonstrate that our proposed method outperforms the current state-of-the-art of simple inverse tone mapping methods and its performance is similar to other more complex and time-consuming advanced techniques

Real-time false-contours removal for inverse tone mapped HDR content

High Dynamic Ranges (HDR) displays can show images with higher color contrast levels and peak luminosities than the commonly used Low Dynamic Range (LDR) displays. Although HDR displays are still expensive, they are reaching the consumer market in the coming years. Unfortunately, most video content is recorded and/or graded in LDR format. Typically, dynamic range expansion by using an Inverse Tone Mapped Operator (iTMO) is required to show LDR content in HDR displays. The most common type of artifact derived from dynamic range expansion is false contouring, which negatively affects the overall image quality. In this paper, we propose a new fast iterative false-contour removal method for inverse tone mapped HDR content. We consider the false-contour removal as a signal reconstruction problem, and we solve it using an iterative Projection Onto Convex Sets (POCS) minimization algorithm. Unlike most other false-contour removal techniques, we define reconstruction constraints taking into account the iTMO used. Experimental results demonstrate the effectiveness of the proposed method to remove false contours while preserving details in the image. In order speed-up the execution time, the proposed method was implemented to run on a GPU. We were able to show that it can be used to remove false contours in real-time from an inverse tone mapped High-definition HDR video sequences at 24 fps

Highlights Analysis System (HAnS) for low dynamic range to high dynamic range conversion of cinematic low dynamic range content

We propose a novel and efficient algorithm for detection of specular reflections and light sources (highlights) in cinematic content. The detection of highlights is important for reconstructing them properly in the conversion of the low dynamic range (LDR) to high dynamic range (HDR) content. Highlights are often difficult to be distinguished from bright diffuse surfaces, due to their brightness being reduced in the conventional LDR content production. Moreover, the cinematic LDR content is subject to the artistic use of effects that change the apparent brightness of certain image regions (e.g. limiting depth of field, grading, complex multi-lighting setup, etc.). To ensure the robustness of highlights detection to these effects, the proposed algorithm goes beyond considering only absolute brightness and considers five different features. These features are: the size of the highlight relative to the size of the surrounding image structures, the relative contrast in the surrounding of the highlight, its absolute brightness expressed through the luminance (luma feature), through the saturation in the color space (maxRGB feature) and through the saturation in white (minRGB feature). We evaluate the algorithm on two different image data-sets. The first one is a publicly available LDR image data-set without cinematic content, which allows comparison to the broader State of the art. Additionally, for the evaluation on cinematic content, we create an image data-set consisted of manually annotated cinematic frames and real-world images. For the purpose of demonstrating the proposed highlights detection algorithm in a complete LDR-to-HDR conversion pipeline, we additionally propose a simple inverse-tone-mapping algorithm. The experimental analysis shows that the proposed approach outperforms conventional highlights detection algorithms on both image data-sets, achieves high quality reconstruction of the HDR content and is suited for use in LDR-to-HDR conversion