Tone mapping

About: Tone mapping is a research topic. Over the lifetime, 1713 publications have been published within this topic receiving 48490 citations.

Real-Time Tone-Mapping Processor with Integrated Photographic and Gradient Compression using 0.13 μm Technology on an Arm Soc Platform

Abstract: Due to recent advances in high dynamic range (HDR) technologies, the ability to display HDR images or videos on conventional LCD devices has become more and more important. Many tone-mapping algorithms have been proposed to meet this end, the choice of which depends on display characteristics such as luminance range, contrast ratio and gamma correction. An ideal HDR tone-mapping processor should have a robust core functionality, high flexibility, and low area consumption, and therefore an ARM-core-based system-on-chip (SOC) platform with a HDR tone-mapping application-specific integrated circuit (ASIC) is suitable for such applications. In this paper, we present a systematic methodology for the development of a tone-mapping processor of optimized architecture using an ARM SOC platform, and illustrate the use of this novel HDR tone-mapping processor for both photographic and gradient compression. Optimization is achieved through four major steps: common module extraction, computation power enhancement, hardware/software partition, and cost function analysis. Based on the proposed scheme, we present an integrated photographic and gradient tone-mapping processor that can be configured for different applications. This newly-developed processor can process 1,024 × 768 images at 60 fps, runs at 100 MHz clock and consumes a core area of 8.1 mm2 under TSMC 0.13 μm technology, resulting in a 50% improvement in speed and area as compared with previously-described processors.

Perceptual Effects in Real-Time Tone Mapping

Abstract: Tremendous progress in the development and accessibility of high dynamic range (HDR) technology that has happened just recently results in fast proliferation of HDR synthetic image sequences and captured HDR video. When properly processed, such HDR data can lead to very convincing and realistic results even when presented on traditional low dynamic range (LDR) display devices. This requires real-time local contrast compression (tone mapping) with simultaneous modeling of important in HDR image perception effects such as visual acuity, glare, day and night vision. We propose a unified model to include all those effects into a common computational framework, which enables an efficient implementation on currently available graphics hardware. We develop a post processing module which can be added as the final stage of any real-time rendering system, game engine, or digital video player, which enhances the realism and believability of displayed image streams.

Learning Based Image Transformation Using Convolutional Neural Networks

Abstract: We have developed a learning-based image transformation framework and successfully applied it to three common image transformation operations: downscaling, decolorization, and high dynamic range image tone mapping. We use a convolutional neural network (CNN) as a non-linear mapping function to transform an input image to a desired output. A separate CNN network trained for a very large image classification task is used as a feature extractor to construct the training loss function of the image transformation CNN. Unlike similar applications in the related literature such as image super-resolution, none of the problems addressed in this paper have a known ground truth or target. For each problem, we reason about a suitable learning objective function and develop an effective solution. This is the first work that uses deep learning to solve and unify these three common image processing tasks. We present experimental results to demonstrate the effectiveness of the new technique and its state-of-the-art performances.

Backward-compatible video capture and distribution

Abstract: Video processing techniques and pipelines that support capture, distribution, and display of high dynamic range (HDR) image data to both HDR-enabled display devices and display devices that do not support HDR imaging. A sensor pipeline may generate standard dynamic range (SDR) data from HDR data captured by a sensor using tone mapping, for example local tone mapping. Information used to generate the SDR data may be provided to a display pipeline as metadata with the generated SDR data. If a target display does not support HDR imaging, the SDR data may be directly rendered by the display pipeline. If the target display does support HDR imaging, then an inverse mapping technique may be applied to the SDR data according to the metadata to render HDR data for display. Information used in performing color gamut mapping may also be provided in the metadata and used to recover clipped colors for display.

Delivering interactivity to complex tone mapping operators

Abstract: The accurate display of high dynamic range images requires the application of complex tone mapping operators. These operators are computationally costly, which prevents their usage in interactive applications. We propose a general framework that delivers interactive performance to an important subclass of tone mapping operators, namely global tone mapping operators. The proposed framework consists of four steps: sampling the input image, applying the tone mapping operator, fitting the point-sampled tone mapping curve, and reconstructing the tone mapping curve for all pixels of the input image. We show how to make use of recent graphics hardware while keeping the advantage of generality by performing tone mapping in software. We demonstrate the capabilities of our method by accelerating several common global tone mapping operators and integrating the operators in a real-time rendering application.