What is the Truth: A Survey of Video Compositing Techniques <s> A. Comparison Among Blending Techniques <s> Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamless importation of both opaque and transparent source image regions into a destination region. The second set is based on similar mathematical ideas and allows the user to modify the appearance of the image seamlessly, within a selected region. These changes can be arranged to affect the texture, the illumination, and the color of objects lying in the region, or to make tileable a rectangular selection. <s> BIB001 </s> What is the Truth: A Survey of Video Compositing Techniques <s> A. Comparison Among Blending Techniques <s> Gradient-domain compositing is an essential tool in computer vision and its applications, e.g., seamless cloning, panorama stitching, shadow removal, scene completion and reshuffling. While easy to implement, these gradient-domain techniques often generate bleeding artifacts where the composited image regions do not match. One option is to modify the region boundary to minimize such mismatches. However, this option may not always be sufficient or applicable, e.g., the user or algorithm may not allow the selection to be altered. We propose a new approach to gradient-domain compositing that is robust to inaccuracies and prevents color bleeding without changing the boundary location. Our approach improves standard gradient-domain compositing in two ways. First, we define the boundary gradients such that the produced gradient field is nearly integrable. Second, we control the integration process to concentrate residuals where they are less conspicuous. We show that our approach can be formulated as a standard least-squares problem that can be solved with a sparse linear system akin to the classical Poisson equation. We demonstrate results on a variety of scenes. The visual quality and run-time complexity compares favorably to other approaches. <s> BIB002
In this subsection, a comparison among the performance of Poisson image editing BIB001 , error-tolerant image compositing BIB002 , and MPB technique  is presented. Flowers dataset that presented in  was used by the blending techniques as a set of target images to analysis the performance of those techniques. The reason of using this dataset is that each image in this dataset contains heavy details and textures. The dataset consists of 1,360 images, where each image contains in average 300000 pixels. Dataset's images are categorized into 17 different categories. Each category contains 80 images. Source images that were used in the comparison are in different sizes. The dataset was scaled up to generate another two datasets for evaluating the performance of blending techniques with different sizes of images. The first new dataset was scaled up 150% from the original size of the dataset, and the second one was scaled up 200% from the original size of the dataset. Table 1 target image, where the process of cloning is applied to all of the target image's pixels.