Using ACES Look Modification Transforms (LMTs) in VFX Environments – Part 2: Gamut Mapping

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].

Improving Detection of Manipulated Passport Photos - Training Course for Border Control Inspectors to Detect Morphed Facial Passport Photos - Part I: Introduction, State-of-the-Art and Preparatory Tests and Experiments

In recent years, ID controllers have observed an increase in the use of fraudulently obtained ID documents [1]. This often involves deception during the application process to get a genuine document with a manipulated passport photo. One of the methods used by fraudsters is the presentation of a morphed facial image. Face morphing is used to assign multiple identities to a biometric passport photo. It is possible to modify the photo so that two or more persons, usually the known applicant and one or more unknown companions, can use the passport to pass through a border control [2]. In this way, persons prohibited from crossing a border can cross it unnoticed using a face morphing attack and thus acquire a different identity. The face morphing attack aims to weaken the application for an identity card and issue a genuine identity document with a morphed facial image. A survey among experts at the Security Printers Conference revealed that a relevant number of at least 1,000 passports with morphed facial images had been detected in the last five years in Germany alone [1]. Furthermore, there are indications of a high number of unreported cases. This high presumed number of unreported cases can also be explained by the lack of morphed photographs’ detection capabilities. Such identity cards would be recognized if the controllers could recognize the morphed facial images. Various studies have shown that the human eye has a minimal ability to recognize morphed faces as such [2], [3], [4], [5], [6]. This work consists of two parts. Both parts are based on the complete development of a training course for passport control officers to detect morphed facial images. Part one contains the conception and the first test trials of how the training course has to be structured to achieve the desired goals and thus improve the detection of morphed facial images for passport inspectors. The second part of this thesis will include the complete training course and the evaluation of its effectiveness.

Improved Content-Color-Dependent Screening (CCDS): Adaptive Bilateral Filtering and Color-Aware Sobel Edge Detector

In previous work [1] , content-color-dependent screening (CCDS) determines the best screen assignments for either regular or irregular haltones to each image segment, which minimizes the perceived error compared to the continuous-tone digital image. The model first detects smooth areas of the image and applies a spatiochromatic HVS-based model for the superposition of the four halftones to find the best screen assignment for these smooth areas. The segmentation is not limited to separating foreground and background. Any significant color regions need to be segmented. Hence, the segmentation method becomes crucial. In this paper, we propose a general segmentation method with a few improvements: The number of K-means clusters is determined by the elbow method to avoid assigning the number of clusters manually for each image. The noise removing bilateral filter is adaptive to each image, so the parameters do not need to be tested and adjusted based on the visual output results. Also, some color regions can be clearly separated out from other color regions by applying a color-aware Sobel edge detector.

Contrast Signal to Noise Ratio

The detection and recognition of objects is essential for the operation of autonomous vehicles and robots. Designing and predicting the performance of camera systems intended to supply information to neural networks and vision algorithms is nontrivial. Optimization has to occur across many parameters, such as focal length, f-number, pixel and sensor size, exposure regime and transmission schemes. As such numerous metrics are being explored to assist with these design choices. Detectability index (SNRI) is derived from signal detection theory as applied to imaging systems and is used to estimate the ability of a system to statistically distinguish objects [1], most notably in the medical imaging and defense fields [2]. A new metric is proposed, Contrast Signal to Noise Ratio (CSNR), which is calculated simply as mean contrast divided by the standard deviation of the contrast. This is distinct from contrast to noise ratio which uses the noise of the image as the denominator [3,4]. It is shown mathematically that the metric is proportional to the idealized observer for a cobblestone target and a constant may be calculated to estimate SNRI from CSNR, accounting for target size. Results are further compared to Contrast Detection Probability (CDP), which is a relatively new objective image quality metric proposed within IEEE P2020 to rank the performance of camera systems intended for use in autonomous vehicles [5]. CSNR is shown to generate information in illumination and contrast conditions where CDP saturates and further can be modified to provide CDP-like results.

Using images of noise to estimate image processing behavior for image quality evaluation

Noise is an extremely important image quality factor. Camera manufacturers go to great lengths to source sensors and develop algorithms to minimize it. Illustrations of its effects are familiar, but it is not well known that noise itself, which is not constant over an image, can be represented as an image. Noise varies over images for two reasons. (1) Noise voltage in raw images is predicted to be proportional to a constant plus the square root of the number of photons reaching each pixel. (2) The most commonly applied image processing in consumer cameras, bilateral filtering [1], sharpens regions of the image near contrasty features such as edges and smooths (applies lowpass filtering to reduce noise) the image elsewhere. Noise is normally measured in flat, uniformly-illuminated patches, where bilateral filter smoothing has its maximum effect, often at the expense of fine detail. Significant insight into the behavior of image processing can be gained by measuring the noise throughout the image, not just in flat patches. We describe a method for obtaining noise images, then illustrate an important application— observing texture loss— and compare noise images for JPEG and raw-converted images. The method, derived from the EMVA 1288 analysis of flat-field images, requires the acquisition of a large number of identical images. It is somewhat cumbersome when individual image files need to be saved, but it’s fast and convenient when direct image acquisition is available.

Sourcing and Qualifying Passive Polarised 3D TVs

Millions of Stereoscopic 3D capable TVs were sold into the consumer market from 2007 through to 2016. A wide range of display technologies were supported including rear-projection DLP, Plasma, LCD and OLED. Some displays supported the Active 3D method using liquid-crystal shutter glasses, and some displays supported the Passive 3D method using circularly polarised 3D glasses. Displays supporting Full-HD and Ultra-HD (4K) resolution were available in sizes ranging from 32" to 86" diagonal. Unfortunately display manufacturers eventually changed their focus to promoting other display technologies and 2016 was the last year that new 3D TVs were made for the consumer market. Fortunately, there are still millions of 3D displays available through the secondhand- market, however it can be difficult to know which displays have 3D display support. This paper will provide a listing of specifically Passive 3D TVs manufactured by LG, however it has been our experience that the 3D quality varied considerably from one display to another hence it is necessary to qualify the quality of the 3D available on these displays using a testing technique that will be described in the paper.