What is the Truth: A Survey of Video Compositing Techniques <s> V. COLOR CORRECTION <s> This paper describes a new framework for processing images by example, called “image analogies.” The framework involves two stages: a design phase, in which a pair of images, with one image purported to be a “filtered” version of the other, is presented as “training data”; and an application phase, in which the learned filter is applied to some new target image in order to create an “analogous” filtered result. Image analogies are based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of “image filter” effects, including traditional image filters, such as blurring or embossing; improved texture synthesis, in which some textures are synthesized with higher quality than by previous approaches; super-resolution, in which a higher-resolution image is inferred from a low-resolution source; texture transfer, in which images are “texturized” with some arbitrary source texture; artistic filters, in which various drawing and painting styles are synthesized based on scanned real-world examples; and texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple painting interface. <s> BIB001 </s> What is the Truth: A Survey of Video Compositing Techniques <s> V. COLOR CORRECTION <s> The knowledge of material specific film grain characteristics can significantly improve the performance of digital film processing algorithms. This paper proposes a rapid and reliable detector for film grain properties. First, homogeneous blocks concerning intensity and texture are determined by a novel measure calculated in the frequency domain. Second, based on these blocks the signal-dependent grain noise level is estimated and an image region of pure film grain is detected. Evaluation based on a wide range of structured images with high frequent electronic noise and low frequent film grain, shows that the method performs with a worst case estimation error of 2.54 dB in typical movie quality (PSNR of 25-40 dB). This meets the requirements of real applications such as digital film restoration and special effects compositing. <s> BIB002
The color correction process is performed for making two or more footages, after blending them together, appear as illuminated using the same lighting condition. For each frame in given footages, frame intensity values t depend on the light source L(  ), the reflection of the objects I(x,  ) and the sensitivity function of the camera C(  ) which consists of (C R (  ), t  that depends on the wavelength  of the light, and the spatial coordinate x, where where, m refers to (R, G, B) channels. The visible spectrum is represented by w. n b and n s are the scale factors that control the reflected light at location x. Many techniques were presented for matching different light spaces  . Matching Black and White Points technique  uses two luminance versions of the two footages for matching white and black points of them. The matching process generates a color curve that is applied to other points in the luminance versions of the footages. By matching the light spaces of the two luminance versions, the light spaces of the colored versions of the footages are matched using linear transformations. Grayscale balancing is another technique for the color correction process  . The idea is that in colored images, gray pixels have the same intensity in each channel. Any changes in any of the three channels of a well-known gray pixel are detected and applied to all pixels in the second layer. One of important issues in the color correction process is estimating the source light color. Diagonal transform  is used for estimating the unknown source light's color  using the following equation where F k is the footage that is captured under an unknown light source M and D M, t is a diagonal matrix that is used for mapping colors of pixels in F k to the corresponding colors in the transformed image F t . Quality matching is required for layers that have different qualities. Color quantization techniques, such as median cut algorithm  , are used for matching the number of colors in the layers. Noise detection and extraction techniques are used for filtering any noisy footage, such as  BIB002 . Texture transfer techniques are used for transferring the footage's textures to other footage, such as image analogies technique BIB001 . 