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We present an algorithm to accelerate resolution independent curve rendering on mobile GPUs. Recent trends in graphics hardware have created a plethora of compressed texture formats specific to GPU manufacturers. However, certain implementations of platform independent path rendering require generating grayscale textures on the CPU containing the extent that each pixel is covered by the curve. In this paper, we demonstrate that generating a compressed grayscale texture prior to uploading it to the GPU creates faster rendering times in addition to the memory savings. We implement a real-time compression technique for coverage masks and compare our results against the GPU-based implementation of the highly optimized Skia rendering library. We also analyze the worst case properties of our compression algorithms. We observe up to a 2 $\times$ speed improvement over the existing GPU-based methods in addition to up to a 9:1 improvement in GPU memory gains. We demonstrate the performance on multiple mobile platforms.

Cloud gaming services are heavily dependent on the efficiency of real-time video streaming technology owing to the limited bandwidths of wire or wireless networks through which consecutive frame images are delivered to gamers. Video compression algorithms typically take advantage of similarities among video frame images or in a single video frame image. This paper presents a method for computing and extracting both graphics information and an object's boundary from consecutive frame images of a game application. The method will allow video compression algorithms to determine the positions and sizes of similar image blocks, which in turn, will help achieve better video compression ratios. The proposed method can be easily implemented using function call interception, a programmable graphics pipeline, and off-screen rendering. It is implemented using the most widely used Direct3D API and applied to a well-known sample application to verify its feasibility and analyze its performance. The proposed method computes various kinds of graphics information with minimal overhead.

The paper describes a parallel method for a lossless data compression that uses graphical processing units (GPUs). Two commonly used statistical and dictionary approaches to data compression have been applied in our method. The reduction of compression time was possible due to the implementation of multi level parallel methods that use a single GPU or a set of GPUs efficiently. The base of our method is a search for repetitions in data that is executed in parallel with the use of sorted suffix tables. On the second level of concurrency operations on different data blocks: data file reading, match search, coding, compression and data file writing are performed in parallel. The methods proposed, supplying a comparable compression ratio, achieve a better compression speed than a standard CPU-based compression tools used in personal computers. Experiments performed in technologically comparable systems showed that our approach is similar or even better in terms of power and cost efficiency. We proposed a novel approach to parallel lossless data compression on GPU.The efficiency of the approach is based on parallelization of data processing on different hardware and logical levels.We obtained better compression speed than popular data compression programs used in PC (compression ratio is sustained).Our approach outperforms other known GPU based methods to lossless data compression.

This paper presents a novel method to effectively store three-dimensional (3D) data and 2D texture data into a regular 24-bit image. The proposed method uses the Hilbert space-filling curve to map the normalized unwrapped phase map to two 8-bit color channels, and saves the third color channel for 2D texture storage. By further leveraging existing 2D image and video compression techniques, the proposed method can achieve high compression ratios while effectively preserving data quality. Since the encoding and decoding processes can be applied to most of the current 2D media platforms, this proposed compression method can make 3D data storage and transmission available for many electrical devices without requiring special hardware changes. Experiments demonstrate that if a lossless 2D image/video format is used, both original 3D geometry and 2D color texture can be accurately recovered; if lossy image/video compression is used, only black-and-white or grayscale texture can be properly recovered, but much higher compression ratios (e.g., 1543:1 against the ASCII OBJ format) are achieved with slight loss of 3D geometry quality.

Interacting with real-time rendered 3D content from powerful machines on smaller devices is becoming ubiquitous through commercial products that enable in-home streaming within the same local network. However, support for high resolution, low latency in-home streaming at high image quality is still a challenging problem. To enable this, we enhance an existing open source framework for in-home streaming. We add highly optimized DXT1 (DirectX Texture Compression) support for thin desktop and notebook clients. For rendered light fields, we improve the encoding algorithms for higher image quality. Within a 10 Gigabit Ethernet (10 GbE) network, we achieve streaming up to 5K resolution at 55 frames per second. Through new low-level algorithmic improvements, we increase the compression speed of ETC1 (Ericsson Texture Compression) by a factor of 5. We are the first to bring ETC2 compression to real-time speed, which increases the streamed image quality. Last, we reduce the required data rate by more than a factor of 2 through foveated compression with real-time eye tracking.

Image Compression is a demanding field in this era of communication. There is a need to study and analyze the literature for image compression, as the demand for images, video sequences and computer animation has increased at very high rate so that the increment is drastically over the years. Multimedia data whether graphics, audio, video data which is uncompress requires considerable transmission bandwidth and storage capacity. So this leads to the need of compression of images and all multimedia applications to save storage and transmission time. In this study we discuss different compression algorithms used to reduce size of images without quality reduction.

Low-latency transmissions of high resolution video such as HD, 2K, or 4K over both Internet and private IP networks have found a foothold in many interactive applications, ranging from collaborative environments in science and medicine to the arts and entertainment industry. In this paper we demonstrate how the power of commodity graphics processing units can be used for efficient implementation of JPEG and DXT compression. We propose an approach to fine-grained parallelization of JPEG compression and the use of auxiliary indexes for efficient decompression, which are backward compatible with the JPEG standard. In-depth performance analysis is provided to show various aspects of the proposed parallelization including the dependency on image content and on various settings of the compression algorithm, as well as the impact of compression for interactive applications in terms of end-to-end latency. The applicability of these compression schemes in medicine and cinematography has also been evaluated using double-blind ABX tests compared with uncompressed video. We describe selected successful real-world deployment use cases based on our open-source implementation within the UltraGrid framework, such as trans-Atlantic 4K interactive video streaming during the CineGrid 2011 workshop. As discussed in the paper, the proposed approaches to parallelization provide sufficient performance even for the future generation of 8K video processing systems, currently limited by availability of camera and display hardware.

Embedded systems in several applicationsrequire a graphics system to displaysome application-specific information. Yet, commercial graphic cards for the embedded systems either incur high costs, or they are inconvenient to use. Furthermore, they tend to quickly become obsolete due to the advances in display technology. On the other hand, FPGAs provide reconfigurable hardware resources that can be used to implement graphics system in which they can be reconfigured to meet the ev er-evolving requirements of graphics systems. Motivated from this fact, this study considers the design and implementation of a 2D graphics system on FPGA. The graphics system proposed is composed of a CPU IP core, peripheral IP cores (Bresenham, BitBLT, D DR Memory Controller, and VGA) and PLB bus to which CPU and all peripheral IP cores are attached. Furthermore, some graphics drivers and APIs are developed to complete the whole graphics creation process.

Compression is the science and art of representing the information in a compact form rather than its original or uncompressed form in data storage or transmission. Data compression is storing data in a format that requires less space than usual. Nowadays, data compression is a common requirement in telecommunication industry. Since last few decades, the research is continuing in the field of compression and various data compression techniques are already developed to compress different data formats. But, a data compression program using strange number system (specially using pentaoctagesimal SNS) tries to compact the data in some special format, such that the data occupies less disk space or that it can be transmitted in less time. A decompression program restores the information again. This paper mainly describes a simple compression and decompression process which is free from time complexity.

Data compression is an important area of information and communication technologies it seeks to reduce the number of bits used to store or transmit information. It will efficiently utilizes the memory spaces and allows to transmit data within a limited bandwidth. Most compression process is achieved by removing data redundancy while preserving information content. Data compression algorithms exploit some characteristics to make the compressed data smaller than the original data. Every data compression process is working with well defined algorithm. Data compression on graphics processors (GPUs) has become an effective approach to improve the performance of main memory. CUDA is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance with graphics processing unit (GPU).Data compression algorithms on CUDA provides better compression process. In this paper, we implement the most power full algorithm LZW on CUDA architecture. Due to the parallel characteristics of GPU, compression process time is very less than the CPU environment.

Image compression is a core task for mobile devices, social media and cloud storage backend services. Key evaluation criteria for compression are: the quality of the output, the compression ratio achieved and the computational time (and energy) expended. Predicting the effectiveness of standard compression implementations like libjpeg and WebP on a novel image is challenging, and often leads to non-optimal compression. This paper presents a machine learning-based technique to accurately model the outcome of image compression for arbitrary new images in terms of quality and compression ratio, without requiring significant additional computational time and energy. Using this model, we can actively adapt the aggressiveness of compression on a per image basis to accurately fit user requirements, leading to a more optimal compression. Postprint

In this paper we introduce a new fixed-rate texture compression scheme based on the energy compaction properties of a modified Haar transform. The coefficients of this transform are quantized and stored using standard block compression methods, such as DXTC and BC7, ensuring simple implementation and very fast decoding speeds. Furthermore, coefficients with the highest contribution to the final image are quantized with higher accuracy, improving the overall compression quality. The proposed modifications to the standard Haar transform, along with a number of additional optimizations, improve the coefficient quantization and reduce the compression error. The resulting method offers more flexibility than the currently available texture compression formats, providing a variety of additional low bitrate encoding modes for the compression of grayscale and color textures. © 2012 Wiley Periodicals, Inc.

This paper presents new fixed-rate texture compression systems for RGB and RGBA images, which use variable precision differential encoding for the color codewords, resulting in reduced compression artifacts, in particular for smooth color variations. Experiments using hundreds of textures show that they achieve higher quality than the commonly used S3TC at the same compression ratio or (for RGBA images) similar quality to S3TC at twice the compression ratio. In hardware implementations a large part of decompression hardware can be shared with existing common systems, minimizing the amount of additional hardware needed.

Although volunteer computing with a huge number of high-performance game consoles connected to the Internet is promising to achieve large-scale data mining, the programming models of such game consoles for data mining tasks are restricted. As the game consoles have high-performance graphics hardware for state-of-the-art video games, a key to exploit their computation power for data mining is how effectively the data mining is mapped to the hardware as graphics processes.In this paper, therefore, a popular data mining tool called the backpropagation learning neural network is implemented as an application running on graphics hardware. Since the recent graphics hardware has many vector processing units and high memory bandwidth, it is promising to accelerate the backpropagation learning task involving a lot of data-parallel computations. The evaluation results have demonstrated the great potential of our prototype implementation for massive backpropagation learning tasks. The graphics hardware can efficiently work especially if the task is implemented so as to use data-parallel instructions supported by the hardware.

Current compression schemes for floating-point data commonly take fixed-precision values and compress them to a variable-length bit stream, complicating memory management and random access. We present a fixed-rate, near-lossless compression scheme that maps small blocks of 4(d) values in d dimensions to a fixed, user-specified number of bits per block, thereby allowing read and write random access to compressed floating-point data at block granularity. Our approach is inspired by fixed-rate texture compression methods widely adopted in graphics hardware, but has been tailored to the high dynamic range and precision demands of scientific applications. Our compressor is based on a new, lifted, orthogonal block transform and embedded coding, allowing each per-block bit stream to be truncated at any point if desired, thus facilitating bit rate selection using a single compression scheme. To avoid compression or decompression upon every data access, we employ a software write-back cache of uncompressed blocks. Our compressor has been designed with computational simplicity and speed in mind to allow for the possibility of a hardware implementation, and uses only a small number of fixed-point arithmetic operations per compressed value. We demonstrate the viability and benefits of lossy compression in several applications, including visualization, quantitative data analysis, and numerical simulation.

We present a compound image compression algorithm for real-time applications of computer screen image transmission. It is called shape primitive extraction and coding (SPEC). Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding algorithm and the pictorial pixels with the standard lossy JPEG, respectively. The segmentation first classifies image blocks into picture and text/graphics blocks by thresholding the number of colors of each block, then extracts shape primitives of text/graphics from picture blocks. Dynamic color palette that tracks recent text/graphics colors is used to separate small shape primitives of text/graphics from pictorial pixels. Shape primitives are also extracted from text/graphics blocks. All shape primitives from both block types are losslessly compressed by using a combined shape-based and palette-based coding algorithm. Then, the losslessly coded bitstream is fed into a LZW coder. Experimental results show that the SPEC has very low complexity and provides visually lossless quality while keeping competitive compression ratios.

Three-dimensional numerical modeling of fluid flows is an important research tool to understand many fluid dynamical effects observed in nature. With the strong growth of available computational resources the use of such models has greatly increased over the last years. Because the available mass storage has not increased in the same order as the CPU speed many researchers nowadays face the problem of how to store and transfer the large data sets produced by the model calculations for post-processing. The use of lossy wavelet-based compression techniques on this data has been investigated in several publications. These techniques are often specialized to one problem and are not easy to implement. In the area of digital media, however, advances have been made for still image (JPEG, JPEG-2000) and motion image (MPEG) compression. In this paper we investigate the usefulness of these image compression algorithms for the storage of data from computational fluid dynamics on regular cartesian grids. We analyze both the compression ratios achieved and the error introduced by these lossy compression schemes. We found that, for our purposes, the JPEG compression scheme allows an easy-to-use, portable, robust, and computationally efficient lossy compression. For the easy use of these compression algorithms we present a simple wrapper library.

Texture mapping is an important operation in high-quality computer graphics applications. The principles of image filtering are well established and understood and several high-quality texture filtering algorithms have been developed. However, these have tended to be either off-line software implementations or based on high-end computer graphics rendering systems. Current generation PC-based graphics acceleration utilises texture pre-filtering techniques based upon an isotropic filter kernel. The most common implementations are those based on MIP-map texture storage and bilinear or trilinear interpolation. Such filters give a reduction in image aliasing at the expense of introducing blurring. As acceleration hardware performance improves, tolerance of such compromises is falling leading to the adoption of “anisotropic” filtering solutions. In this paper we present a detailed description of a low-cost implementation of an anisotropic filtering unit for use within a contemporary graphics pipeline. The filter is shown to produce improved visible results for only a modest increase in hardware cost.

Advanced broadcast manipulation of TV sequences and enhanced user interfaces for TV systems have resulted in an increased amount of pre- and post-editing of video sequences, where graphical information is inserted. However, in the current broadcasting chain, there are no provisions for enabling an efficient transmission/storage of these mixed video and graphics signals and, at this emerging stage of DTV systems, introducing new standards is not desired. Nevertheless, in the professional video communication chain between content provider and broadcaster and locally, in the DTV receiver, proprietary video-graphics compression schemes can be used to enable more efficient transmission/storage of mixed video and graphics signals. For example, in the DTV receiver case this will lead to a significant memory-cost reduction. To preserve a high overall image quality, the video and graphics data require independent coding systems, matched with their specific visual and statistical properties. We introduce various efficient algorithms that support both the lossless (contour, runlength and arithmetic coding) and the lossy (block predictive coding) compression of graphics data. If the graphics data are a-priori mixed with video and the graphics position is unknown at compression time, an accurate detection mechanism is applied to distinguish the two signals, such that independent coding algorithms can be employed for each data-type. In the DTV memory-reduction scenario, an overall bit-rate control completes the system, ensuring a fixed compression factor of 2-3 per frame without sacrificing the quality of the graphics.

Digital images have become an important source of information in the modern world of communication systems. In their raw form, digital images require a tremendous amount of memory. Many research efforts have been devoted to the problem of image compression in the last two decades. Two different compression categories must be distinguished: lossless and lossy. Lossless compression is achieved if no distortion is introduced in the coded image. Applications requiring this type of compression include medical imaging and satellite photography. For applications such as video telephony or multimedia applications, some loss of information is usually tolerated in exchange for a high compression ratio. In this two-part paper, the major building blocks of image coding schemes are overviewed. Part I covers still image coding, and Part II covers motion picture sequences. In this first part, still image coding schemes have been classified into predictive, block transform, and multiresolution approaches. Predictive methods are suited to lossless and low-compression applications. Transform-based coding schemes achieve higher compression ratios for lossy compression but suffer from blocking artifacts at high-compression ratios. Multiresolution approaches are suited for lossy as well for lossless compression. At lossy high-compression ratios, the typical artifact visible in the reconstructed images is the ringing effect. New applications in a multimedia environment drove the need for new functionalities of the image coding schemes. For that purpose, second-generation coding techniques segment the image into semantically meaningful pairs. Therefore, parts of these methods have been adapted to work for arbitrarily shaped regions. In order to add another functionality, such as progressive transmission of the information, specific quantization algorithms must he defined. A final step in the compression scheme is achieved by the codeword assignment. Finally, coding results are presented which compare state-of-the-art techniques for lossy and lossless compression. The different artifacts of each technique are highlighted and discussed. Also, the possibility of progressive transmission is illustrated.

Data compression is the process of representing information in a compact form, in order to reduce the storage requirements and, hence, communication bandwidth. It has been one of the critical enabling technologies for the ongoing digital multimedia revolution for decades. In the variable-length encoding (VLE) compression method, most frequently occurring symbols are replaced by codes with shorter lengths. As it is a common strategy in many compression applications, efficient parallel implementations of VLE are very desirable. In this paper we present CUVLE, a GPU implementation of VLE on CUDA. Our approach is on average more than 20 and 2 times faster than the corresponding CPU serial implementation and the only known state-of-the-art GPU implementation, respectively. Junta de Andalucía, TIC-1692. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

International audience; New 3D applications such as 3DTV and FVV require not only a large amount of data, but also high-quality visual rendering. Based on one or several depth maps, intermediate views can be synthesized using a depth image-based rendering technique. Many compression schemes have been proposed for texture-plus-depth data, but the exploitation of the correlation between the two representations in enhancing compression performances is still an open research issue. In this paper, we present a novel compression scheme that aims at improving the depth coding using a joint depth/texture coding scheme. This method is an extension of the LAR (Locally Adaptive Resolution) codec, initially designed for 2D images. The LAR coding framework provides a lot of functionalities such as lossy/lossless compression, low complexity, resolution and quality scalability and quality control. Experimental results address both lossless and lossy compression aspects, considering some state of the art techniques in the two domains (JPEGLS, JPEGXR). Subjective results on the intermediate view synthesis after depth map coding show that the proposed method significantly improves the visual quality.

Mapping textures onto surfaces of computer-generated objects is a technique which greatly improves the realism of their appearance. Unfortunately, this imposes high computational demands and, even worse, tremendous memory bandwidth requirements on the graphics system. Tight cost frames in the industry in conjunction with ever increasing user expectations make the design of a powerful texture mapping unit a difficult task. To meet these requirements we follow two different approaches. On the technology side, we observe a rapidly emerging technology which offers the combination of enormous transfer rates and computing power: logic-embedded memories. On the algorithmic side, a common way to reduce data traffic is image compression. Its application to texture mapping, however, is difficult since the decompression must be done at pixel frequency. In this work we will focus on the latter approach, describing the use of a specific compression scheme for texture mapping. It allows the use of a very simple and fast decompression hardware, bringing high performance texture mapping to low-cost systems.

We present a new fixed-rate texture compression scheme based on the energy compaction properties of the Discrete Wavelet Transform. Targeting existing commodity graphics hardware and APIs, our method is using the DXT compression formats to perform the quantization and storage of the wavelet transform coefficients, ensuring very fast decoding speeds. An optimization framework minimizes the quantization error of the coefficients and improves the overall compression quality. Our method provides a variety of low bitrate encoding modes for the compression of grayscale and color textures. These encoding modes offer either improved quality or reduced storage over the DXT1 format. Furthermore, anisotropic texture filtering is performed efficiently with the help of the native texture hardware. The decoding speed and the simplicity of the implementation make our approach well suited for use in games and other interactive applications.

International audience; Many processing operations are nowadays applied on 3D meshes like compression, watermarking, remeshing and so forth; these processes are mostly driven and/or evaluated using simple distortion measures like the Hausdorff distance and the root mean square error, however these measures do not correlate with the human visual perception while the visual quality of the processed meshes is a crucial issue. In that context we introduce a full-reference 3D mesh quality metric; this metric can compare two meshes with arbitrary connectivity or sampling density and produces a score that predicts the distortion visibility between them; a visual distortion map is also created. Our metric outperforms its counterparts from the state of the art, in term of correlation with mean opinion scores coming from subjective experiments on three existing databases. Additionally, we present an application of this new metric to the improvement of rate-distortion evaluation of recent progressive compression algorithms.

Graphics Processing Units have become a booster for the microelectronics industry. However, due to intellectual property issues, there is a serious lack of information on implementation details of the hardware architecture that is behind GPUs. For instance, the way texture is handled and decompressed in a GPU to reduce bandwidth usage has never been dealt with in depth from a hardware point of view. This work addresses a comparative study on the hardware implementation of different texture decompression algorithms for both conventional (PCs and video game consoles) and mobile platforms. Circuit synthesis is performed targeting both a reconfigurable hardware platform and a 90nm standard cell library. Area-delay trade-offs have been extensively analyzed, which allows us to compare the complexity of decompressors and thus determine suitability of algorithms for systems with limited hardware resources.

In this work, a real time hardware data compression solution for raster scan cameras, to be onboard the next generation of Remote Sensing Brazilian satellites, is proposed. The options for image data compression methods are briefly covered to substantiate the choice: a low complexity implementation based on JPEG-LS, near-lossless compression algorithm, which can be synthesized in a single electronic device. JPEG-LS performance in terms of compression rates and loss is evaluated by processing remote sensing rough images through in-house developed software tool. Finally, the description and results of an implementation in FPGA are presented and compared to other works, emphasizing the differences related to application requirements.

In this paper, we propose a novel compound video compression method for real-time applications of computer screen video transmission. The compound video is a very special kind of video, which usually has less motion and contains a mixture of text, graphics, and natural pictures in each frame. A variety of algorithms has been proposed for compound image compression, which tries to remove the spatial redundancy of a single image. However, few works have addressed the problem of compound videos compression. Therefore, we review the previous works on compound image compression and discuss how to extend the existing algorithms to compound video compression. We propose a new video compression framework based on H.264. In order to improve the visual performance and network efficiency, we propose an adaptive quantization algorithm and a rate control algorithm for compound video compression. The experimental results show that the new compression method is able to compress the compound video in real-time with a relatively low computational complexity and with the motion compensation, the visual quality of the compound video is much better than simple compound image compression.

This paper presents a novel method for using programmable graphics hardware to generate fringe patterns for SLM-based holographic displays. The algorithm is designed to take the programming constraints imposed by the graphics hardware pipeline model into consideration, and scales linearly with the number of object points. In contrast to previous methods we do not have to use the Fresnel approximation. The technique can also be used on several graphics processors in parallel for further optimization. We achieve real-time frame rates for objects consisting of a few hundred points at a resolution of 960 × 600 pixels and over 10 frames per second for 1000 points.

High dynamic range (HDR) images are increasingly employed in games and interactive applications for accurate rendering and illumination. One disadvantage of HDR images is their large data size; unfortunately, even though solutions have been proposed for future hardware, commodity graphics hardware today does not provide any native compression for HDR textures.In this paper, we perform extensive study of possible methods for supporting compressed HDR textures on commodity graphics hardware. A desirable solution must be implementable on DX9 generation hardware, as well as meet the following requirements. First, the data size should be small and the reconstruction quality must be good. Second, the decompression must be efficient; in particular, bilinear/trilinear/anisotropic texture filtering ought to be performed via native texture hardware instead of custom pixel shader filtering.We present a solution that optimally meets these requirements. Our basic idea is to convert a HDR texture to a custom LUVW space followed by an encoding into a pair of 8-bit DXT textures. Since DXT format is supported on modern commodity graphics hardware, our approach has wide applicability. Our compression ratio is 3:1 for FP16 inputs, allowing applications to store 3 times the number of HDR texels in the same memory footprint. Our decompressor is efficient and can be implemented as a short pixel program. We leverage existing texturing hardware for fast decompression and native texture filtering, allowing HDR textures to be utilized just like traditional 8-bit DXT textures. Our reduced data size has a further advantage: it is even faster than rendering from uncompressed HDR textures due to our reduced texture memory access. Given the quality and efficiency, we believe our approach suitable for games and interactive applications.

Spherical harmonic (SH) basis functions have been widely used for representing spherical functions in modeling various illumination properties. They can compactly represent low-frequency spherical functions. However, when the unconstrained least square method is used for estimating the SH coefficients of a hemispherical function, the magnitude of these SH coefficients could be very large. Hence, the rendering result is very sensitive to quantization noise (introduced by modern texture compression like S3TC, IEEE half float data type on GPU, or other lossy compression methods) in these SH coefficients. Our experiments show that, as the precision of SH coefficients are reduced, the rendered images may exhibit annoying visual artifacts. To reduce the noise sensitivity of the SH coefficients, this paper first discusses how the magnitude of SH coefficients affects the rendering result when there is quantization noise. Then, two fast fitting methods for estimating the noise-resistant SH coefficients are proposed. They can effectively control the magnitude of the estimated SH coefficients and, hence, suppress the rendering artifacts. Both statistical and visual results confirm our theory.

This research is motivated by the needs of robust streaming of vector graphics contents over the Internet, wireless and other lossy networks. We present a multiple description coding (MDC) technique for error resilient compression and transmission of 2D vector graphics contents. An object is coded into two or more so-called co-descriptors, which are transmitted in separate data packets and generally via different network routes from a server to a client. Each co-descriptor can autonomously provide an approximation of the input object, and it can collaborate with other co-descriptors, if also available at the decoder, to refine the approximation.

It is possible to improve the features supported by devices with embedded systems by increasing the processor computing power, but this always results in higher costs, complexity, and power consumption. An interesting alternative is to use the growing networking infrastructures to do remote processing and visualization, with the embedded system mainly responsible for communications and user interaction. This enables devices to behave as if much more “intelligent” to users, at very low costs and power. In this article we explain how compression can make some of these solutions more bandwidth-efficient, enabling devices to simply decompress very rich graphical information and user interfaces that had been rendered elsewhere. The mixture of natural images and video with text, graphics, and animations simultaneously in the same frame is called compound video. We present a new method for compression of compound images and video, which is able to efficiently identify the different components during compression, and use an appropriate coding method. Our system uses lossless compression for graphics and text, and, on natural images and highly detailed parts, it uses lossy compression with dynamically varying quality. Since it was designed for embedded systems with very limited resources, and it has small executable size, and low complexity for classification, compression and decompression. Other compression methods (e.g., MPEG) can do the same, but are very inefficient for compound content. High-level graphics languages can be bandwidth-efficient, but are much less reliable (e.g., supporting Asian fonts), and are many orders of magnitude more complex. Numerical tests show the very significant gains in compression achieved by these systems.

The new image compression standard, JPEG2000, provides higher compression rates for the same visual quality for grayscale and color images than JPEG. JPEG2000 is being adopted for image compression and transmission, eg, in mobile telephones, PDA, and wearable computers. Depending on the application, images may contain formatted text and graphics data. For graphics images at the same low bit rates, graphics compression methods, such as the graphics interchange format (GIF) and portable network graphics (PNG), outperform JPEG2000 in visual quality. In this paper, we describe problems associated with compressing graphics data with JPEG2000, and propose modifications to a JPEG2000 encoder to minimize distortion for color graphics data by using a model of the human visual system. The visual improvements are quantified by a new measure of distortion.

Current methods of compressing 3D animation for transmission over the Internet tend to be of very low quality. Quality is sacrificed to keep bandwidth low when using lossy compression techniques such as MPEG, Cinepak, or more recent formats such as Vivo and VDOnet. While these techniques can be effective approaches to all-purpose video compression, it should be possible to achieve better performance with the knowledge that the source consists of 3D animation. A new technique is proposed, which instead of focusing on compressing the rendered frames of the animation sequence, compresses and optimizes the original source scene data for streaming, and renders the 3D scene in real time on the client system.

Many polygon meshes have properties such as shading normals, colours, texture coordinates, and/or material attributes that are associated with the vertices, faces or corners of the mesh. While current research in mesh compression has focused on connectivity and geometry coding, the compression of properties has received less attention. There are two kinds of information to compress. One specifies each individual property: the property values. The other describes how the properties are attached to the mesh: the property mapping. The authors introduce a predictive compression scheme for the property mapping that is 2 to 10 times more compact than previously reported methods.

Data compression is very necessary in business data processing, because of the cost savings that it offers and the large volume of data manipulated in many business applications. It is a method or system for transmitting a digital image (i.e., an array of pixels) from a digital data source to a digital data receiver. More the size of the data be smaller, it provides better transmission speed and saves time. In this communication, we always want to transmit data efficiently and noise freely. This paper will provide some compression techniques for lossless text type data compression and comparative result of multiple and single compression, that will help to find out better compression output and to develop compression algorithms.

This paper presents a reconfigurable machine for applications in image or video compression. The machine can be used stand alone or as a universal accelerator co-processor for desktop computers for image processing. It is well suited for image compression algorithms such as JPEG for still pictures or for encoding MPEG movies. It provides a much cheaper and more flexible hardware platform than special image compression ASICs and it can substantially accelerate desktop computing.

Multimedia applications require a compression and decompression scheme for digital video. The standardized and widely used techniques JPEG and MPEG provide very good compression ratios, but are computationally quite complex and demanding. We propose to use an extension to the much simpler Color Cell Compression scheme as an alternative. Our extension includes the use of variable block sizes, the reuse of color index values from previously encoded blocks, and Huffman encoding of the stream of blocks. We present experimental results showing that our scheme provides much better runtime performance than MPEG, at the cost of a slightly inferior compression ratio. It is thus especially suited for software videos in high-speed networks.

This paper describes the implementation of a data compression scheme for an interactive colour graphics terminal in which the storage requirement is reduced in comparison with a full 1:1 bit map system. The paper also discusses the effect of data compression on the system design. Compression is performed in software using a microprocessor, and decompression of the picture code into raster scan form is performed in real time by hardware. The technique allows complex graphics pictures to be displayed at low cost.