Tone-mapped Low Dynamic Range Research Articles

TMO-Det: Deep tone-mapping optimized with and for object detection

Detecting objects in challenging illumination conditions is critical for autonomous driving. Existing solutions detect objects with standard or tone-mapped Low Dynamic Range (LDR) images. In this paper, we propose a novel adversarial approach that jointly optimizes tone-mapping (mapping High Dynamic Range (HDR) to LDR) and object detection. We analyze different ways to combine the feedback from tone-mapping quality and object detection quality for training such an adversarial network. We show that our deep tone-mapping operator jointly trained with an object detector achieves the best tone-mapping quality as well as detection quality compared to alternative approaches.

Differential Evolution-based Approach for Tone-Mapping of High Dynamic Range Images

Recently, high dynamic range (HDR) imaging has received significant attention from research community as well as the industrial companies due to valuable applications of HDR images in better visualization and analysis. However, HDR images need to be converted to low dynamic range (LDR) images for viewing on standard LDR display screens. Several tone-mapping operators have been proposed for the conversion, however, so far, no significant works have been reported employing artificial intelligence to achieve better enhancement of the output images. In this paper, we present an optimization-based approach, to enhance the quality of the tone-mapped LDR images using metaheuristics. More specifically, the optimization process is based on the differential evolution (DE) algorithm which takes tone-mapping function of an existing histogram-based method as initial guess and refines the histogram bins iteratively leading to progressive enhancement of the quality of LDR image. The final results produced by the proposed optimized histogram-based approach (OHbA) showed better performance compared to the existing state-of-the-art tone-mapping algorithms.

No-Reference Image Quality Metric for Tone-Mapped Images

Tone-mapping operators transform high dynamic range (HDR) images into displayable low dynamic range (LDR) images. Image quality evaluation of these LDR images is not possible by comparison with their corresponding high dynamic range images. Hence, a no-reference image quality metric for tone-mapped LDR images is proposed based on the fitting to the present psychophysical results including different visual image quality attributes. Ten images, including HDR natural scenes, were tonemapped using six TMOs. They were used in the assessment and visual attributes were determined to predict the quality of these images. The visual attributes (brightness and Naturalness) were modeled using parameters derived from CAM16-UCS. Results showed that the quality prediction of the model had a reasonable degree of accuracy.

An optimization framework for inverse tone mapping using a single low dynamic range image

Conventional inverse tone-mapping (ITM) methods tend to produce contrast distortions such as contrast loss and contrast reversal in reconstructed high dynamic range (HDR) images. This paper proposes a novel ITM optimization framework based on the assumption that the input low dynamic range (LDR) image is similar to the LDR image obtained by tone mapping a true HDR image. In the proposed framework, an HDR image is initially reconstructed by applying a conventional tone-mapping function in a reverse manner, and then the reconstructed HDR image is iteratively modified toward the optimum HDR image by minimizing the difference between the input LDR image and a tone-mapped LDR image obtained from the reconstructed HDR image. The experimental results demonstrate that the proposed framework effectively reconstructs a high-quality HDR image and outperforms other conventional methods in terms of objective quality.

High dynamic range image tone mapping based on asymmetric model of retinal adaptation

Global tone mapping operators using the symmetrical retinal response model to light tend to produce a low dynamic range (LDR) image that exhibits loss of details of its corresponding high dynamic range (HDR) image in a bright or dark area. In this paper, we introduce a new asymmetric sigmoid curve (ASC) based on the model of retinal adaptation encompassing symmetrical S-shaped curve, and present two global tone mapping operators by using the ASC. In the proposed method, an ASC-based tone mapping function is obtained by using a well-known classic photography technique, called zone system. In addition, a contrast-enhancing tone mapping function is introduced by formulating a bi-criteria optimization problem with the luminance histogram of an input HDR image and the ASC-based mapping function. Experimental results demonstrate that the proposed method enhances the global contrast while preserving image details in the tone-mapped LDR image. Moreover, the objective assessment results using an image quality metric indicate that the proposed method shows a high performance to state-of-the-art global tone mapping operators.

Improvement of JPEG XT Floating-Point HDR Image Coding Using Region Adaptive Prediction

Displays capable of representing high-dynamic range (HDR) images were recently released in the digital consumer electronics market, and consumers have become increasingly interested in HDR images. However, the majority of today’s digital imaging devices are still low dynamic range (LDR) using an 8-bit representation for each RGB color components; hence, a backward-compatible HDR image format with an existing LDR-based imaging environment is required. Accordingly, JPEG XT, a new HDR image coding standard providing JPEG backward compatibility was established by the JPEG committee based on the market needs. JPEG XT consists of a base and a residual layer. Both layers are independently encoded by a legacy JPEG encoder. Most importantly, the base layer contains a tone-mapped LDR version of the HDR image for compliance with a legacy JPEG decoder. To date, three profiles have been defined for JPEG XT to encode HDR images, which are represented using floating-point values. This paper proposes a new image-coding scheme that uses a region adaptive prediction method with modified current specifications on JPEG XT Part 7. The proposed method adaptively predicts the HDR pixel value using different prediction information for each block based on the ratio between the LDR and HDR blocks, which is invariant to the tone-mapping operator, while the existing JPEG XT profiles globally predict the HDR values using an inverse transformation of the LDR values. Experimental results using various sample images show the superiority of the proposed method in terms of objective and visual evaluations.

A Novel Data Hiding Algorithm for High Dynamic Range Images

In this paper, we propose a novel data hiding algorithm for high dynamic range (HDR) images encoded by the OpenEXR file format. The proposed algorithm exploits each of three 10-bit mantissa fields as an embedding unit in order to conceal k bits of a secret message using an optimal base which produces the least pixel variation. An aggressive bit encoding and decomposition scheme is recommended, which offers a high probability to convey ( k + 1) bits without increasing the pixel variation caused by message concealment. In addition, we present a bit inversion embedding strategy to further increase the capacities when the probability of appearance of secret bit “1” is greater than 0.5. Furthermore, we introduce an adaptive data hiding approach for concealing more secret messages in pixels with low luminance, exploiting the features of the human visual system to achieve luminance-aware adaptive data hiding. The stego HDR images produced by our algorithm coincide with the HDR image file format, causing no suspicion from malicious eavesdroppers. The generated stego HDR images and their tone-mapped low dynamic range (LDR) images reveal no perceptual differences when subjected to quantitative testing by visual difference predictor. Our algorithm can resist steganalytic attacks from the HDR and LDR RS and SPAM steganalyzers. We present the first data hiding algorithm for OpenEXR HDR images offering a high embedding rate and producing high visual quality of the stego images. Our algorithm outperforms the current state-of-the-art works.

Context-dependent JPEG backward-compatible high-dynamic range image compression

High-dynamic range (HDR) imaging is expected, together with ultrahigh definition and high-frame rate video, to become a technology that may change photo, TV, and film industries. Many cameras and displays capable of capturing and rendering both HDR images and video are already available in the market. The popularity and full-public adoption of HDR content is, however, hindered by the lack of standards in evalu- ation of quality, file formats, and compression, as well as large legacy base of low-dynamic range (LDR) displays that are unable to render HDR. To facilitate the wide spread of HDR usage, the backward compatibility of HDR with commonly used legacy technologies for storage, rendering, and compression of video and images are necessary. Although many tone-mapping algorithms are developed for generating viewable LDR con- tent from HDR, there is no consensus of which algorithm to use and under which conditions. We, via a series of subjective evaluations, demonstrate the dependency of the perceptual quality of the tone-mapped LDR images on the context: environmental factors, display parameters, and image con- tent itself. Based on the results of subjective tests, it proposes to extend JPEG file format, the most popular image format, in a backward compat- ible manner to deal with HDR images also. An architecture to achieve such backward compatibility with JPEG is proposed. A simple implementation of lossy compression demonstrates the efficiency of the proposed archi- tecture compared with the state-of-the-art HDR image compression. © 2013

Binocular tone mapping

By extending from monocular displays to binocular displays, one additional image domain is introduced. Existing binocular display systems only utilize this additional image domain for stereopsis. Our human vision is not only able to fuse two displaced images, but also two images with difference in detail, contrast and luminance, up to a certain limit. This phenomenon is known as binocular single vision . Humans can perceive more visual content via binocular fusion than just a linear blending of two views. In this paper, we make a first attempt in computer graphics to utilize this human vision phenomenon, and propose a binocular tone mapping framework. The proposed framework generates a binocular low-dynamic range (LDR) image pair that preserves more human-perceivable visual content than a single LDR image using the additional image domain. Given a tone-mapped LDR image (left, without loss of generality), our framework optimally synthesizes its counterpart (right) in the image pair from the same source HDR image. The two LDR images are different, so that they can aggregately present more human-perceivable visual richness than a single arbitrary LDR image, without triggering visual discomfort . To achieve this goal, a novel binocular viewing comfort predictor (BVCP) is also proposed to prevent such visual discomfort. The design of BVCP is based on the findings in vision science. Through our user studies, we demonstrate the increase of human-perceivable visual richness and the effectiveness of the proposed BVCP in conservatively predicting the visual discomfort threshold of human observers.

Rate-distortion optimized layered coding of high dynamic range videos

An efficient algorithm to compress high dynamic range (HDR) videos into layered bitstreams is proposed in this work. First, we separate an HDR video sequence into a tone-mapped low dynamic range (LDR) sequence and a ratio sequence, which represents ratios between HDR and LDR pixel values. Then, we encode the LDR and ratio sequences to maximize the rate-distortion (R–D) performance by extending the standard H.264/AVC codec. Specifically, we estimate the distortion of the HDR sequence from those of the LDR sequence and the ratio sequence, and then allocate a limited bit budget to the LDR sequence and the ratio sequence efficiently to maximize the qualities of both LDR and HDR sequences. Conventional LDR devices use only the LDR stream, whereas HDR devices reconstruct the HDR video from the LDR and ratio streams. Simulation results show that the proposed algorithm provides significantly better R-D performance than conventional HDR video coding techniques.