Figure 2 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
CIE 1931 chromaticity diagram (y axis shown) with comparison between ACES (black), professional digital motion picture cameras (red) and reference cinema/display (blue) color gamuts.
Source publication
The Academy of Motion Picture Arts and Sciences has been pivotal in the inception, design and later adoption of a vendor-agnostic and open framework for color management, the Academy Color Encoding System (ACES), targeting theatrical, TV and animation features, but also still-photography and image preservation at large. For this reason, the Academy...
Contexts in source publication
Context 1
... leads to a plethora of new color-spaces whose optical/digital definitions are, at best, only partially disclosed by camera manufacturers, e.g., ARRI LogC Wide Gamut, RED DRAGONColorm REDLogFilmn/Gamman, Sony S-Logm S-Gamman (m,n = 1,2,3 according to the camera manufacturers' color-science versions or generation of the sensors), Panasonic V-Log, GoPro CineForm/ProTune™, etc., cfr. Figure 2 and [17]. The native plates for most of the cinematic content are thus born digitally in these partially undefined spaces, but have one thing in common: they use a scene-referred colorimetry [3,18]. ...
Context 2
... and monitors (not only those in the grading theatre/room) are all color-calibrated according to their own reference color standards, dynamic ranges and viewing environments, cfr. Table 1 (and Figure 2). For multiple-delivery shows (e.g., theatrical, web, then SVoD and TV, both HDR and SDR), several color-grading sessions are needed, because there is not yet a universally-accepted HDR-to-SDR gamut-mapping algorithm, [19], that is satisfactory from the creative standpoint-and automatically scales to different shows. ...
Context 3
... AP1 gamut (Figure 8) was introduced in ACES 1.0 to overcome the first two drawbacks above; it is a subset of AP0, it does not cover the whole standard observer's, yet it is "HDR wide gamut", like ARRI WideGamut, DCI P3 and Rec.2020 (Table 2). In fact, AP1 contains P3 and Rec.2020 gamut used in D-Cinema and HDR reference displays (Figure 2). It represents colors that are viewable on displays and projectors, including HDR, without noticeable color skew. ...
Context 4
... is, therefore, up to camera vendors to provide conversion formula from their sensors' native colorimetries into ACES, cfr. Figure 2 and Section 2.1. Any color transformation that takes a non-ACES color-space into ACES2065-1 is called an (ACES) Input Transform (also abbreviated in IDT, whose acronym comes from its deprecated, pre-release name: input device transform). ...
Context 5
... Output Transform is the composition of a fixed conversion from scene-to output-referred colorimetry, by means of a Reference Rendering Transform (RRT), whose formula closely reselbles that of a tonecurve, plus an output device transform (ODT) which maps the output-referred CVs out of the RRT into specific colorimetry standards, cfr. Section 2.3 and Figure 2. ...
Citations
... The film and television industry is known as the "dream factory". It has always relied on infinite creative imagination and exquisite production technology, the unreal world of light and shadow story narrated for the audience to bring the double feast of visual and auditory [4][5]. Since entering the 21st century, this dream factory has also quietly undergone unprecedented changes. ...
This paper studies the framework of an AI intelligent editing system, explores the functional modules of intelligent editing, deeply analyses the use of AI editing in film and television post-production, and carries out an in-depth study of AI editing methods. Then, we established an impact effect analysis model using the analysis of variance, t-test, and regression analysis methods. Based on this model, we compared the audience viewing data for AI editing and ordinary editing, analyzed the audience’s emotional tendency and participation, and studied the impact of AI editing on the effects of film and television post-production. The study demonstrates that AI editing has a significant promotion effect on creative freedom, content and theme fit, cost-effectiveness, and diversity of content in film and television works, with significance coefficients of 0.046, 0.023, 0.012, and 0.006, respectively. This study holds significant value in promoting the integration and development of AI technology in film and television, as well as strengthening its application in the field of digital media.
... We apply an exponential activation to the light environment map to better reflect its HDR (High Dynamic Range) nature. I phy is tone-mapped by the ACES Filmic tone mapping curve [44] and converted to the sRGB color space before image loss computation. The normal of each Gaussian is defined as its shortest axis facing the camera, which follows 3DGS-DR [33]. ...
We propose progressive radiance distillation, an inverse rendering method that combines physically-based rendering with Gaussian-based radiance field rendering using a distillation progress map. Taking multi-view images as input, our method starts from a pre-trained radiance field guidance, and distills physically-based light and material parameters from the radiance field using an image-fitting process. The distillation progress map is initialized to a small value, which favors radiance field rendering. During early iterations when fitted light and material parameters are far from convergence, the radiance field fallback ensures the sanity of image loss gradients and avoids local minima that attracts under-fit states. As fitted parameters converge, the physical model gradually takes over and the distillation progress increases correspondingly. In presence of light paths unmodeled by the physical model, the distillation progress never finishes on affected pixels and the learned radiance field stays in the final rendering. With this designed tolerance for physical model limitations, we prevent unmodeled color components from leaking into light and material parameters, alleviating relighting artifacts. Meanwhile, the remaining radiance field compensates for the limitations of the physical model, guaranteeing high-quality novel views synthesis. Experimental results demonstrate that our method significantly outperforms state-of-the-art techniques quality-wise in both novel view synthesis and relighting. The idea of progressive radiance distillation is not limited to Gaussian splatting. We show that it also has positive effects for prominently specular scenes when adapted to a mesh-based inverse rendering method.
... Lookup-table (LUT) is often used to match the colors of the input device with the output device [40,47]. Various color profiles, such as ACES [1], ACEScc [25], ARRI K1S1 [7], RED IPP2 1 , T-CAM v2 or T-Log 2 , are often used to adjust the colors of the captured video or still images to reach specific artistic intentions and enhance the color-grading process. Blender 3 modeling software implemented a new AgX profile for wide gamut and spectral rendering. ...
This paper investigates the impact of image color profile on the visual quality in light field rendering. Light field rendering methods require several input views, capturing the same scene from different camera positions. These views can be captured in real life by cameras or rendered from synthetic 3D scenes. A novel view can be synthesized from the input views, capturing the scene from a virtual camera position. Modern cinematography makes use of various color profiles that affect how the measured light on the camera sensor is translated into the color values of the digital image. Different profiles can be used for different purposes, be it artistic or pragmatic to reveal necessary details. The main scientific question of this paper is if certain color profiles can lead to better quality of the novel view synthesis in light field rendering. The paper shows that logarithmic profiles can significantly improve the visual quality. Three light field rendering methods from different categories were used to compare 17 widely used color profiles. Additional measurements show that post processing and color grading needs to be applied at the very end of the rendering pipeline to ensure the best quality. Also, the effect of denoising algorithms on light field rendering is measured.
... With Cineon and other innovations, the traditional cinematographic workflow, centered around film negatives, underwent a significant shift with digital technologies, encompassing negative scanning and digital camera acquisition. In the absence of a standardized color management system for the diverse sources originating from various digital motion picture cameras and film, the ACES system was developed [4], [5]. ACES system was initiated in 2004 through collaboration with the leading industry technologists and primarily aims to streamline the complexities of managing numerous file formats, image encoding, metadata transfer, color reproduction, and image exchanges within the contemporary motion picture workflow. ...
... We adopt the same loss function from Mip-NeRF to constrain the estimated radiance via volume rendering. To convert HDR radiance output to LDR value, we apply empirical tone-mapping (Arrighetti 2017;Chen, Wang, and Liu 2022), clipping (into [0, 1]), and gamma correction (factor of 2.2) on the output radiance to obtain mapped LDR value. MSE loss L r for the radiance fields is calculated in coarse and fine phases as, ...
Panoramic imaging research on geometry recovery and High Dynamic Range (HDR) reconstruction becomes a trend with the development of Extended Reality (XR). Neural Radiance Fields (NeRF) provide a promising scene representation for both tasks without requiring extensive prior data. How- ever, in the case of inputting sparse Low Dynamic Range (LDR) panoramic images, NeRF often degrades with under-constrained geometry and is unable to reconstruct HDR radiance from LDR inputs. We observe that the radiance from each pixel in panoramic images can be modeled as both a signal to convey scene lighting information and a light source to illuminate other pixels. Hence, we propose the irradiance fields from sparse LDR panoramic images, which increases the observation counts for faithful geometry recovery and leverages the irradiance-radiance attenuation for HDR reconstruction. Extensive experiments demonstrate that the irradiance fields outperform state-of-the-art methods on both geometry recovery and HDR reconstruction and validate their effectiveness. Furthermore, we show a promising byproduct of spatially-varying lighting estimation. The code is available at https://github.com/Lu-Zhan/Pano-NeRF.
... Finally, the averaging step of ray energies takes place in the linear high-dynamic range (HDR) color space. To obtain a color between range [0, 1] in the standarddynamic range (SDR) space, a tone mapping [36] step should be applied to the averaged color. ...
Ray tracing algorithm is a category of rendering algorithms that calculate pixel colors by simulating light rays in parallel. The quantum supersampling has achieved a quadratic speedup over classical Monte Carlo method, but its output image contains many detached abnormal noisy dots. In this paper, we improve quantum supersampling by replacing the QFT-based phase estimation in quantum supersampling with a robust quantum counting scheme. We do simulation experiments to show that the quantum ray tracing with improved quantum supersampling does perform better than classical path tracing algorithm as well as the original form of quantum supersampling.
... And in quantum ray tracing, the time cost is evaluated by the number of queries Finally, the averaging step of ray energies takes place in the linear high-dynamic range (HDR) color space. To obtain a color between range [0, 1] in the standard-dynamic range (SDR) space, a tone mapping [Arr17] step should be applied to the averaged color. In summary, the framework of quantum ray tracing is illustrated in Fig. 6. ...
Ray tracing algorithm is a category of rendering algorithms that calculate the color of pixels by simulating the physical movements of a huge amount of rays and calculating their energies, which can be implemented in parallel. Meanwhile, the superposition and entanglement property make quantum computing a natural fit for parallel tasks.Here comes an interesting question, is the inherently parallel quantum computing able to speed up the inherently parallel ray tracing algorithm? The ray tracing problem can be regarded as a high-dimensional numerical integration problem. Suppose N queries are used, classical Monte Carlo approaches has an error convergence of , while the quantum supersampling algorithm can achieve an error convergence of approximately O(1/N). However, the outputs of the origin form of quantum supersampling obeys a probability distribution that has a long tail, which shows up as many detached abnormal noisy dots on images. In this paper, we improve quantum supersampling by replacing the QFT-based phase estimation in quantum supersampling with a robust quantum counting scheme, the QFT-based adaptive Bayesian phase estimation. We quantitatively study and compare the performances of different quantum counting schemes. Finally, we do simulation experiments to show that the quantum ray tracing with improved quantum supersampling does perform better than classical path tracing algorithm as well as the original form of quantum supersampling.
... Doing this also bounds the per-pixel error = ( ) − ( ), suppressing outliers and making the optimization more robust in scenes with high dynamic range. We illustrate this improvement in Fig. 4c, where an ACES [Arrighetti 2017] tone-mapping operator is applied to the optimized image. Optimizing w.r.t. the original perceptual error (4) yields a noisy and overly dark image compared to the tone-mapped ground truth. ...
Realistic image synthesis involves computing high-dimensional light transport integrals which in practice are numerically estimated using Monte Carlo integration. The error of this estimation manifests itself in the image as visually displeasing aliasing or noise. To ameliorate this, we develop a theoretical framework for optimizing screen-space error distribution. Our model is flexible and works for arbitrary target error power spectra. We focus on perceptual error optimization by leveraging models of the human visual system's (HVS) point spread function (PSF) from halftoning literature. This results in a specific optimization problem whose solution distributes the error as visually pleasing blue noise in image space. We develop a set of algorithms that provide a trade-off between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using both quantitative and perceptual error metrics to support our analysis, and provide extensive supplemental material to help evaluate the perceptual improvements achieved by our methods.
... For example, if two cameras made by different manufacturers were placed side-by-side and recorded the same scene, there will be a visible difference in color between the two shots. All cameras have their formats/colorimetries "designed to account for camera sensors' characteristics" (Arrighetti, 2017). An agnostic than linear color spaces do. ...
... Examples of different Although the camera provides an initial color space, for television broadcasts color grading does not occur in-camera but in an entirely separate process during post-production. Color grading is typically "set in a TV grading room, dimly lit and equipped with one or more monitors… that are all color-calibrated according to their own reference color standards, dynamic ranges, and viewing environments" (Arrighetti, 2017). The footage has a raw format or log color space, which was embedded from the camera. ...
... The footage has a raw format or log color space, which was embedded from the camera. The footage was read from this proprietary format and color corrected from widest-gamut color space possible (Arrighetti, 2017). ...
... For example, CIELAB is a color system defined by the Commission Internationale de l'Eclairage (CIE, 2019); it expresses color as three values [L* for lightness ranging from black (0) to white (100), a*) (denoted by A) ranging from green (−) to red (+), and b*) (denoted by B) from blue (−) to yellow (+)] Linear extensions of RGB are noted in the present dataset as "Lin.sRGBx". The Academy Color Encoding System (ACES) is a color system advocated by the still and motion picture industry (Arrighetti, 2017; Academy of Motion Picture Arts and Sciences, 2019). It features six color spaces (AP0 Red, AP0 Green, AP0 Blue, AP1 Red, AP1 Green, AP1 Blue); the AP0 represents the smallest set of primaries that includes the entire CIE 1964 standard-observer spectral locus, while the AP1 is conceived with primaries "bent" to be closer to those of display-referred color spaces. ...
Coal is an important natural resource for global energy production. However, certain types of coal (e.g., lignite) often contain abundant sulfur (S) which can lead to gaseous sulfur dioxide (SO2) emissions when burned. Such emissions subsequently create sulfuric acid (H2SO4), thus causing highly acidic rain which can alter the pH of soil and surface waters. Traditional laboratory analysis (e.g., dry combustion) is commonly used to characterize the S content of lignite, but such approaches are laborious and expensive. By comparison, proximal sensing techniques such as portable X-ray fluorescence (PXRF) spectrometry, visible near infrared (VisNIR) spectroscopy, and optical sensors (e.g., NixPro) can acquire voluminous data which has been successfully used to elucidate fundamental chemistry in a wide variety of matrices. In this study, four active lignite mines were sampled in North Dakota, USA. A total of 249 samples were dried, powdered, then subjected to laboratory-based dry combustion analysis and scanned with the NixPro, VisNIR, and PXRF sensors. 75% of samples (n=186) were used for model calibration, while 25% (n=63) were used for validation. A strong relationship was observed between dry combustion and PXRF S content (r=0.90). Portable X-ray fluorescence S and Fe as well as various NixPro color data were the most important variables for predicting S content. When using PXRF data in isolation, random forest regression produced a validation R2 of 0.80 in predicting total S content. Combining PXRF + NixPro improved R2 to 0.85. Dry combustion S + PXRF S and Fe correctly identified the source mine of the lignite at 55.42% via discriminant analysis. Adding the NixPro color data to the PXRF and dry combustion data, the location classification accuracy increased to 63.45%. Even with VisNIR reflectance values of 10-20%, spectral absorbance associated with water at 1,940 nm was still observed. Principal component analysis was unable to resolve the mine source of the coal in PCA space, but several NixPro vectors were closely clustered. In sum, the combination of the NixPro optical sensor with PXRF data successfully augmented the predictive capability of S determination in lignite ex-situ. Future studies should extend the approach developed herein to in-situ application with special consideration of moisture and matrix efflorescence effects.