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Various web-based image-editing tools and web-based collaborative tools exist in isolation. Research focusing to bridge the gap between these two domains is sparse. We respond to the above and develop prototype groupware for real-time collaborative editing of raster and vector images in a web browser. To better understand the requirements, we conduct a preliminary user study and establish communication and synchronization as key elements. The existing groupware for text documents or presentations handles the above through well-established techniques. However, those cannot be extended as it is for raster or vector graphics manipulation. To this end, we develop a document model that is maintained by a server and is delivered and synchronized to multiple clients. Our prototypical implementation is based on a scalable client–server architecture: using WebGL for interactive browser-based rendering and WebSocket connections to maintain synchronization. We evaluate our work qualitatively through a post-deployment user study for three different scenarios. For quantitative evaluation, we perform a thorough performance measure on both client and server side, thereby identifying design recommendations for future concurrent image-editing software(s). [ABSTRACT FROM AUTHOR]

We introduce a differentiable rasterizer that bridges the vector graphics and raster image domains, enabling powerful raster-based loss functions, optimization procedures, and machine learning techniques to edit and generate vector content. We observe that vector graphics rasterization is differentiable after pixel prefiltering. Our differentiable rasterizer offers two prefiltering options: an analytical prefiltering technique and a multisampling anti-aliasing technique. The analytical variant is faster but can suffer from artifacts such as conflation. The multisampling variant is still efficient, and can render high-quality images while computing unbiased gradients for each pixel with respect to curve parameters. We demonstrate that our rasterizer enables new applications, including a vector graphics editor guided by image metrics, a painterly rendering algorithm that fits vector primitives to an image by minimizing a deep perceptual loss function, new vector graphics editing algorithms that exploit well-known image processing methods such as seam carving, and deep generative models that generate vector content from raster-only supervision under a VAE or GAN training objective. [ABSTRACT FROM AUTHOR]

Block Truncation Coding (BTC) image compression technique is capable of extraordinary computational efficiency for high-framerate configurations under the trend of high image resolution. However, the recent development such as the Dot-Diffused BTC (DDBTC) method still encounters various challenges of visual artifacts, including impulsive noise, false contours and blocking effects. In this study, a new method, the Near-Aperiodic DDBTC (NADDBTC) technique, is proposed to analyze the tradeoff and balance between computational efficiency and visual artifacts. It is mainly because of the quantization levels and the co-optimized Class Matrix (CM) and Diffused Matrix (DM) during image compression procedure. In addition, the tiling method for bitmap generation is optimized for aperiodic compressed results. Experimental results show that this method is capable of excellent image quality and visual perception, as well as processing efficiency, at a level similar to the DDBTC technique by taking advantage of parallel processing in dot diffusion. [ABSTRACT FROM AUTHOR]

We present an interactive approach for decompositing bitmap drawings and studio photographs into opaque and semi-transparent vector layers. Semi-transparent layers are especially challenging to extract, since they require the inversion of the non-linear compositing equation. We make this problem tractable by exploiting the parametric nature of vector gradients, jointly separating and vectorising semi-transparent regions. Specifically, we constrain the foreground colours to vary according to linear or radial parametric gradients, restricting the number of unknowns and allowing our system to efficiently solve for an editable semi-transparent foreground. We propose a progressive workflow, where the user successively selects a semi-transparent or opaque region in the bitmap, which our algorithm separates as a foreground vector gradient and a background bitmap layer. The user can choose to decompose the background further or vectorise it as an opaque layer. The resulting layered vector representation allows a variety of edits, such as modifying the shape of highlights, adding texture to an object or changing its diffuse colour. [ABSTRACT FROM AUTHOR]

To render 3-D graphics efficiently, rasterization techniques have been developed. Traditional triangle traversal techniques using scan-line-based or edge-equation-based methods may cause potential instability from the division operations. This paper develops an efficient rasterization algorithm—a barycentric-based ladder start tile traversal that is division free. Throughout the process, no extra traversal position and context is produced to reduce the number of pixel tests and improve the efficiency and stability of the graphic rendering. It also presents the architecture of a 300 MHz 3-D graphics rasterizer in a 65 nm 1P9M process with a core size of 0.537 mm \(^{2}\) and internal buffer 3 K. The proposed ladder start tile traversal architecture can perform each tile intersect test in six cycles and tile interior traversal with barycentric tests in 2 pixels per cycle. In addition, the rasterizer throughput can achieve up to 50M triangles per second and 600M pixels per second. [ABSTRACT FROM PUBLISHER]

We introduce a novel Metropolis rendering algorithm that directly computes image gradients, and reconstructs the final image from the gradients by solving a Poisson equation. The reconstruction is aided by a low-fidelity approximation of the image computed during gradient sampling. As an extension of path-space Metropolis light transport, our algorithm is well suited for difficult transport scenarios. We demonstrate that our method outperforms the state-of-the-art in several well-known test scenes. Additionally, we analyze the spectral properties of gradient-domain sampling, and compare it to the traditional image-domain sampling. [ABSTRACT FROM AUTHOR]

This paper presents a raster simulator software capable of performing Image Processing applications using Cellular Neural Network (CNN). The CNN paradigm has rapidly evolved to cover a wide range of applications. The basic structure simulator is based on high -- performance software capable of efficiently dealing with large images in the order of 105 pixels. The simulator operates in sequential mode. This provides an added flexibility to create individual templates that can be applied on single color layer to obtain full mix of colors. To perform any kind of color image processing, a color model is selected. Two color mapping schemes are used that effectively assign states to distinct color hues. Image processing with CNN may not always yield the desired visual results and post processing becomes necessary to enhance the visualization of the image. The simulator runs in an X-windows environment and uses standard graphics format as input. [ABSTRACT FROM AUTHOR]

Presents a bit allocation scheme based on visual properties. Through the analysis of visual properties, corresponding visual importance estimates of segmented regions are made. As a result, the important regions of interest (ROD will be ascertained and allocated in order to the quantified ROI values which reflect the importance of each region. Experimental results show that the reconstructed image quality of the video coder produced by the algorithm accord with human subjective conclusion well, and the algorithm have desirable application value and low computation cost. [ABSTRACT FROM AUTHOR]

In many video based applications, it is essential to precisely control the bit rate of video streams for transmission over different networks and channel bandwidth. One critical element in a bit rate control algorithm is the bit production model that predicts the number of produced bits when a certain quantization parameter is used. In this paper, we present a novel bit allocation and rate control algorithm for compressed domain video transcoding. The specific transcoding issue mentioned is referred to as bit rate adaptation or rate shaping. We first review the architectures of different bit rate adaptation transcoders and the generic rate control problem. Then, we propose and formulate an approximate linear bit allocation model, which is based on experimental results. Based on this model, we propose an adaptive bit estimation and allocation scheme for video transcoding. Implementation results show that the proposed algorithm can provide accurate bit allocation, stable buffer occupancy and improved video quality as compared to existing approaches. This rate control scheme can be used to provide flexible video bit rate adaptation and stable transmission of video streams over heterogeneous networks. [Copyright &y& Elsevier]

Discusses a study which proposed an approach to color image compression which involves the absolute moment block truncation coding (AMBTC) and the single bitmap generation scheme. Planes that compose a color image; Focus of AMBTC; Advantages and disadvantages of the use of AMBTC; Process of using two-layer absolute moment block truncation coding.

We study 100 images of early LHC collisions that were recorded by the ATLAS experiment and made public for outreach purposes, and extract the charged particle multiplicity as a function of momentum for proton–proton collisions at a centre-of-mass energy of 7 TeV. As these collisions have already been pre-processed by the ATLAS Collaboration, the particle tracks are visible, but are available to the public only in the form of low-resolution bitmaps. We describe two separate image processing methods, one based on the industry-standard OpenCV library and C++, another based on self-developed algorithms in Python. We present our analysis of the transverse momentum and azimuthal angle distributions of the particles, in agreement with the literature. [ABSTRACT FROM AUTHOR]

The raster scan method in visual information processing is the rectangular pattern of image capture and reconstruction which systematically covers the area progressively, one line at a time starting from the top left corner. This traditional scanning method might work well for still image processing but for moving images, a scanning method such as diagonal snake scan can provide a better object correlation in a sequence of image frames. A novel adaptive scanning method for object-oriented visual information processing is proposed. Here, image pixels are collected starting fromthe centre of the image area and progressively extended outwards similar to the water drop ripple effect. Simulation results have shown that the proposed work uses up to 33% less processing time to cover the region-of-interest in an image compared to the raster scan method. Preliminary simulation results have also shown that with the proposed adaptive scanning methodology, there is a significant improvement in the performance of the object-oriented visual compression. [ABSTRACT FROM AUTHOR]