In modern moving image production pipelines, it is unavoidable to move the footage through different color spaces. Unfortunately, these color spaces exhibit color gamuts of various sizes. The most common problem is converting the cameras’ widegamut color spaces to the smaller gamuts of the display devices (cinema projector, broadcast monitor, computer display). So it is necessary to scale down the scene-referred footage to the gamut of the display using tone mapping functions [34].In a cinema production pipeline, ACES is widely used as the predominant color system. The all-color compassing ACES AP0 primaries are defined inside the system in a general way. However, when implementing visual effects and performing a color grade, the more usable ACES AP1 primaries are in use. When recording highly saturated bright colors, color values are often outside the target color space. This results in negative color values, which are hard to address inside a color pipeline. "Users of ACES are experiencing problems with clipping of colors and the resulting artifacts (loss of texture, intensification of color fringes). This clipping occurs at two stages in the pipeline: <list list-type="simple"> <list-item>- Conversion from camera raw RGB or from the manufacturer’s encoding space into ACES AP0</list-item> <list-item>- Conversion from ACES AP0 into the working color space ACES AP1" [1]</list-item> </list>The ACES community established a Gamut Mapping Virtual Working Group (VWG) to address these problems. The group’s scope is to propose a suitable gamut mapping/compression algorithm. This algorithm should perform well with wide-gamut, high dynamic range, scene-referred content. Furthermore, it should also be robust and invertible. This paper tests the behavior of the published GamutCompressor when applied to in- and out-ofgamut imagery and provides suggestions for application implementation. The tests are executed in The Foundry’s Nuke [2].
Images captured at low light suffers from underexposure and noise. These poor-quality images act as hindrance for computer vision algorithms as well as human vision. While this problem can be solved by increasing the exposure time, it also introduces new problems. In applications like ADAS, where there are fast moving objects in the scene, increasing the exposure time will cause motion blur. In applications, that demand higher frame rate, increasing the exposure time is not an option. Increasing the gain will result in noise as well as saturation of pixels at higher end. So, a real time scene adaptive algorithm is required for the enhancement of low light images. We propose a real time low light enhancement algorithm with more detail preservation compared to existing global based enhancement algorithms for low cost embedded platforms. The algorithm is integrated to image signal processing pipeline of TI’s TDA3x and achieved ˜50fps on c66x DSP for HD resolution video captured from Omnivision’s OV10640 Bayer image sensor.
Contrast enhancement which is an important part of digital image processing has been studied for a long time and widely used in various fields such as digital photography or medical imaging. The purpose of contrast enhancement is to improve the overall contrast of the image and details on the local area. Contrast enhancement algorithms are classified into histogram based methods, tone mapping based methods, and retinex theory based methods. Particularly, retinex theory is widely applied at the spatial domain contrast enhancement. In this paper, we propose the contrast enhancement algorithm using the estimated illumination. Different from conventional retinex based algorithms, the estimated illumination serves as the tone mapping criterion and masked with original image. The intensity of estimated illumination image is adaptively modulated according to original image to improve the contrast of image effectively. Experimental results show that both global and local contrast are enhanced simultaneously with the proposed algorithm. Objective assessment using performance metrics also shows that the proposed method has the highest scores compared to the conventional methods.