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Pre-calculated depth information is essential for efficient light field video rendering, due to the prohibitive cost of depth estimation from color when real-time performance is desired. Standard state-of-the-art video codecs fail to satisfy such performance requirements when the amount of data to be decoded becomes too large. In this paper, we propose a depth image and video codec based on block compression, that exploits typical characteristics of depth streams, drawing inspiration from S3TC texture compression and geometric wavelets. Our codec offers very fast hardware-accelerated decoding that also allows partial extraction for view-dependent decoding. We demonstrate the effectiveness of our codec in a number of multi-view 360 degree video datasets, with quantitative analysis of storage cost, reconstruction quality and decoding performance.

Most state-of-the-art compression algorithms use complex connectivity traversal and prediction schemes, which are not efficient enough for online compression of large meshes. In this paper we propose a scalable massively parallel approach for compression and decompression of large triangle meshes using the GPU. Our method traverses the input mesh in a parallel breadth-first manner and encodes the connectivity data similarly to the well known cut-border machine. Geometry data is compressed using a local prediction strategy. In contrast to the original cut-border machine, we can additionally handle triangle meshes with inconsistently oriented faces. Our approach is more than one order of magnitude faster than currently used methods and achieves competitive compression rates.

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.

Multiresolution Hierarchies (MH) and Directed Acyclic Graphs (DAG) are two recent approaches for the compression of high-resolution shadow information. In this paper, we introduce Merged Multiresolution Hierarchies (MMH), a novel data structure that unifies both concepts. An MMH leverages both hierarchical homogeneity exploited in MHs, as well as topological similarities exploited in DAG representations. We propose an efficient hash-based technique to quickly identify and remove redundant subtree instances in a modified relative MH representation. Our solution remains lossless and significantly improves the compression rate compared to both preceding shadow map compression algorithms, while retaining the full run-time performance of traditional MH representations.

The quality of shadow mapping is traditionally limited by texture resolution. We present a novel lossless compression scheme for high-resolution shadow maps based on precomputed multiresolution hierarchies. Traditional multiresolution trees can compactly represent homogeneous regions of shadow maps at coarser levels, but require many nodes for fine details. By conservatively adapting the depth map, we can significantly reduce the tree complexity. Our proposed method offers high compression rates, avoids quantization errors, exploits coherency along all data dimensions, and is well-suited for GPU architectures. Our approach can be applied for coherent shadow maps as well, enabling several applications, including high-quality soft shadows and dynamic lights moving on fixed-trajectories.

Raster images are the standard format for texture mapping, but they suffer from limited resolution. Vector graphics are resolution-independent but are less general and more difficult to implement on a GPU. We propose a hybrid representation called vector regression functions (VRFs), which compactly approximate any point-sampled image and support GPU texture mapping, including random access and filtering operations. Unlike standard GPU texture compression, (VRFs) provide a variable-rate encoding in which piecewise smooth regions compress to the square root of the original size. Our key idea is to represent images using the multilayer perceptron , allowing general encoding via regression and efficient decoding via a simple GPU pixel shader. We also propose a content-aware spatial partitioning scheme to reduce the complexity of the neural network model. We demonstrate benefits of our method including its quality, size, and runtime speed.

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.

The fundamental limitations of texture mapping has been a long standing problem in computer graphics. The cost of defining and maintaining texture coordinates and the seams that introduce various filtering inconsistencies lead some graphics applications to adapt alternative techniques that directly address these problems, such as mesh colors. However, alternatives to texture mapping introduce run-time cost that contradicts with the performance constraints of real-time graphics applications. In this paper we introduce mesh color textures that offer all benefits of mesh colors to real-time graphics applications with strict performance constraints. Mesh color textures convert the mesh color data to a format that can be efficiently used by the texture filtering hardware on current GPUs. Utilizing a novel 4D texture coordinate formulation, mesh color textures can provide correct filtering for all mipmap levels and eliminate artifacts due to seams. We show that mesh color textures introduce negligible run-time cost with no discontinuity in texture filtering. We also discuss potential future modifications to graphics hardware and API that would further simplify the use of mesh color textures in real-time graphics applications.

With the rapid increase of image sensors resolution, some pressure moved on compression algorithms again. Fractal compression is known to provide good performance in terms of compression ratio. But more than ever, the focus is on execution time of these algorithms. Large and fast access memories are hard to provide and very expensive. Therefore, a fast compression algorithm can reduce the expensive memory dimension if the execution time can handle real-time requirements. Graphics Processing Units (GPUs) could provide support for such goal. This article presents some discussion and an example of how fractal compression algorithms can benefit from GPU support.

The choice of parameters for procedural textures to achieve a desired appearance poses a challenging problem even for experienced artists. We propose a method to automatically determine such parameters to reproduce the appearance of input images. Addressing two-tone textures, we separate the estimation of color and structure information and interpret the problem as image retrieval task from the space of procedural outputs. Applying a perceptually motivated image metric based on a texture descriptor enables us to precompute a comprehensive collection of possible parameter sets and yet achieve interactive retrieval performance. Our method supports a large variety of procedural texture models with a unified approach.

The popularity of multimedia applications has resulted in development of lossless and lossy compression techniques. Many image compression methods clustered under these two compression techniques are discussed briefly in this article. In addition to this context, the survey paper gives a study of the recent algorithms that are available for coding low Depth-of-Field (DOF) images and also covers its extension for depth map image sequence coding. Motivation behind this work is to provide a detailed analysis of these algorithms such as the methodology used, merits and demerits, and the objective and subjective comparison of these algorithms with the standard compression algorithms like JPEG, JPEG 2000, H.261/AVC etc. Further, the paper concludes with a guideline for the new researchers in this field which concerns the design of an efficient compression method for low DOF images and depth map image sequences.

The Video Electronics Standards Association (VESA®) has standardized on a visually lossless, low-complexity, hardware-based compression algorithm for use over display links. This paper summarizes novel features of this new compression standard and provides some evaluation results.

Lossy compression of motion capture data can alleviate the problems of efficient storage and transmission by exploiting the redundancy and the superfluous precision of the data. When considering the acceptable amount of distortion, perceptual issues have to be taken into account. Current state of the art methods reduce the data rate required for high quality storage of motion capture data using various techniques. Most of them, however, do not use the common tools of general data compression, such as the method of Lagrange multipliers, and thus they obtain sub-optimal results, making it difficult to do a fair comparison of their performance.

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.

Graphics compression is a new research area that is relatively new compared to other media types. Less attention has been devoted to 3D object and shape compression from research, industry, and standardization efforts, until recently when usage and complexities of 3D objects has grown in a variety of industries. The need for graphics compression cannot be ignored anymore. In this paper we study and analyze the graphics compression techniques and their relation to other compression techniques. We specifically study and propose the desirable graphics compression technique which leads to more efficient graphics transmission.

Bidirectional Texture Functions (BTFs) are among the highest quality material representations available today and thus well suited whenever an exact reproduction of the appearance of a material or complete object is required. In recent years, BTFs have started to find application in various industrial settings and there is also a growing interest in the cultural heritage domain. BTFs are usually measured from real-world samples and easily consist of tens or hundreds of gigabytes. By using data-driven compression schemes, such as matrix or tensor factorization, a more compact but still faithful representation can be derived. This way, BTFs can be employed for real-time rendering of photo-realistic materials on the GPU. However, scenes containing multiple BTFs or even single objects with high-resolution BTFs easily exceed available GPU memory on today’s consumer graphics cards unless quality is drastically reduced by the compression. In this paper, we propose the Bidirectional Sparse Virtual Texture Function, a hierarchical level-of-detail approach for the real-time rendering of large BTFs that requires only a small amount of GPU memory. More importantly, for larger numbers or higher resolutions, the GPU and CPU memory demand grows only marginally and the GPU workload remains constant. For this, we extend the concept of sparse virtual textures by choosing an appropriate prioritization, finding a trade off between factorization components and spatial resolution. Besides GPU memory, the high demand on bandwidth poses a serious limitation for the deployment of conventional BTFs. We show that our proposed representation can be combined with an additional transmission compression and then be employed for streaming the BTF data to the GPU from from local storage media or over the Internet. In combination with the introduced prioritization this allows for the fast visualization of relevant content in the users field of view and a consecutive progressive refinement.

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.

Collaborative slide image viewing systems are becoming increasingly important in pathology applications such as telepathology and E-learning. Despite rapid advances in computing and imaging technology, current digital pathology systems have limited performance with respect to remote viewing of whole slide images on desktop or mobile computing devices. In this paper we present a novel digital pathology client-server system that supports collaborative viewing of multi-plane whole slide images over standard networks using multi-touch-enabled clients. Our system is built upon a standard HTTP web server and a MySQL database to allow multiple clients to exchange image and metadata concurrently. We introduce a domain-specific image-stack compression method that leverages real-time hardware decoding on mobile devices. It adaptively encodes image stacks in a decorrelated colour space to achieve extremely low bitrates 0.8i¾?bpp with very low loss of image quality. We evaluate the image quality of our compression method and the performance of our system for diagnosis with an in-depth user study.

The compression of binary bitmap images based on statistical coding with context simulation is considered. Development trends for models and methods of context simulation for an efficient algorithm of compressing binary graphics are discussed. An efficient algorithm of compressing binary graphics is presented.

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.

Because of their high peak performance and energy efficiency, massively parallel accelerators such as graphics processing units (GPUs) are quickly spreading in high-performance computing, where large amounts of floating-point data are processed, transferred, and stored. Such environments can greatly benefit from data compression if it is done quickly enough. Unfortunately, most conventional compression algorithms are unsuitable for highly parallel execution. In fact, how to design good compression algorithms for massively parallel hardware still is not fully understood. To remedy this situation, we studied millions of lossless compression algorithms for single- and double-precision floating-point values built exclusively from easily parallelizable components. We analyzed the best of these algorithms, explained why they compress well, and derived the Massively Parallel Compression algorithm from them. This algorithm requires little internal state, achieves heretofore unreached compression ratios (CRs) on several data sets, and roughly matches the best CPU-based algorithms in CR while outperforming them by one to two orders of magnitude in throughput and energy efficiency.

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.

Most of Frame Buffer Compression (FBC) methods are based on variable-length compression algorithm. The proposed fixed-ratio frame buffer compressor, which is based on S3 texture compression (S3TC) algorithm, compresses the recently rendered image into DXT format so that it can be employed as input compressed texture. We have optimized the frame buffer compressor to achieve real time performance and FPGA implementation, which occupies 406 slice registers and 1133 slice LUT in Xilinx Spartan6 FPGA. The compressor consumes 10% additional silicon space, but it reduces the bandwidth consumption to 16.8%–45.8% compared to that of 50–90% by the variable-length compression methods.

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

Most texture compression schemes are not able to accurately represent the object edges, since the number of color candidates usually are not sufficient to reconstruct a smooth block. We propose a new hierarchical palette approach for the texture compression. The colors are chosen from palettes and sub-palettes in quad-tree without increasing bit rates. The experimental results show significant improvements over most existing texture compression methods.

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.

In lossless image compression, many prediction methods are proposed so far to achieve better compression performance/complexity trade off. In this paper, we concentrate on some well-known and widely used low-complexity algorithms exploited in many modern compression systems, including MED, GAP, Graham, Ljpeg, DARC, and GBSW. This paper proposes a new gradient-based tracking and adapting technique that outperforms some existing methods. This paper aims to design an efficient highly adaptive predictor that can be incorporated in modeling step of image compression systems. This claim is proved by testing the proposed method upon a wide variety of images with different characteristics. Six special sets of images including face, sport, texture, sea, text, and medical constitute our dataset.

High-quality texture minification techniques, including trilinear and anisotropic filtering, require texture data to be arranged into a collection of pre-filtered texture maps called mipmaps. In this paper, we present a compression scheme for mipmapped textures which achieves much higher quality than current native schemes by exploiting image coherence across mipmap levels. The basic idea is to use a high-quality native compressed format for the upper levels of the mipmap pyramid (to retain efficient minification filtering) together with a novel compact representation of the detail provided by the highest-resolution mipmap. Key elements of our approach include delta-encoding of the luminance signal, efficient encoding of coherent regions through texel runs following a Hilbert scan, a scheme for run encoding supporting fast random-access, and a predictive approach for encoding indices of variable-length blocks. We show that our scheme clearly outperforms native 6:1 compressed texture formats in terms of image quality while still providing real-time rendering of trilinearly filtered textures. © 2012 Wiley Periodicals, Inc.

Large-scale remote sensing images, including both satellite and aerial photographs, are widely used to render terrain scenes in real-time geographic visualization systems. Such systems often require large memories in order to store fine terrain details and fast network speeds to transfer image data, if they are built as web applications. In this paper, we propose a progressive texture compression framework to reduce the memory and bandwidth cost by compressing repeated content within and among large-scale remote sensing images. Different from existing image factorization methods, our algorithm incrementally find similar regions in new images so that large-scale images can be more efficiently compressed over time. We further propose a descriptor, the Gray Split Rotate (GSR) descriptor, to accelerate the similarity search. The reconstruction quality is finally improved by compressing residual error maps using customized S3TC-like compression. Our experiment shows that even with the error maps, our system still has higher compression rate and higher compression quality than using S3TC alone, which is a typical compression solution in most existing visualization systems.

Power consumption is the key design factor for graphics processing units (GPUs), especially for mobile applications. The increasing bandwidth required to produce more realistic graphics is a major power draw. To address this factor, in this paper, we present a new universal buffer compression method that can handle both color and depth data with the same hardware unit. In contrast to the current state-of-art technologies, which mainly focus on achieving higher and higher compression ratios but discarded the decompression latency, our method reaches a good compromise between the two, which are factors critical to system performance. With spatial prediction and bitstream rearrangement, the data dependencies between different samples are reduced, which enables a parallel decoding process and makes the proposed system have 6.78 times lower decoding latency. Moreover, by adopting a similar concept for the color/depth compression in the DXT5 texture compression method, better quality in terms of PSNR can be achieved without introducing any decoding latency when retrieving a texel.

Texture mapping has been widely used to improve the quality of 3D rendered images. To reduce the storage and bandwidth impact of texture mapping, compression systems are commonly used. To further increase the quality of the rendered images, texture filtering is also often adopted. These two techniques are generally considered to be independent. First, a decompression step is executed to gather texture samples, which is then followed by a separate filtering step. We have investigated a system based on linear transforms that merges both phases together. This allows more efficient decompression and filtering at higher compression ratios. This paper formally presents our approach for any linear transformation, how the commonly used discrete cosine transform can be adapted to this new approach, and how this method can be implemented in real time on current-generation graphics cards using shaders. Through reuse of the existing hardware filtering, fast magnification and minification filtering is achieved. Our implementation provides fully anisotropically filtered samples four to six times faster than an implementation using two separate phases for decompression and filtering. Additionally, our transform-based compression also provides increased and variable compression ratios over standard hardware compression systems at a comparable or better quality level.

In this paper, we proposed an improved texture compression method for graphics hardware. We first give a detail introduction for the texture compression methods which are popular now. Then, an MIPMap-based texture compression method is proposed that is hybrid compression scheme. For smooth area, we can only use 2Bytes to represent the 4*4 pixels block by bilinear interpolation. Otherwise, we will use iPACKMAN algorithm to deal with the noise areas. Actually, this method is feasible to be implemented by hardware since it is very simple. Experimental results show that our method can achieve high compress ratio and high image quality.

Variable bit rate compression can achieve better quality and compression rates than fixed bit rate methods. None the less, GPU texturing uses lossy fixed bit rate methods like DXT to allow random access and on-the-fly decompression during rendering. Changes in games and GPUs since DXT was developed make its compression artifacts less acceptable, and texture bandwidth less of an issue, but texture size is a serious and growing problem. Games use a large total volume of texture data, but have a much smaller active set. We present a new paradigm that separates GPU decompression from rendering. Rendering is from uncompressed data, avoiding the need for random access decompression. We demonstrate this paradigm with a new variable bit rate lossy texture compression algorithm that is well suited to the GPU, including a new GPU-friendly formulation of range decoding, and a new texture compression scheme averaging 12.4:1 lossy compression ratio on 471 real game textures with a quality level similar to traditional DXT compression. The total game texture set are stored in the GPU in compressed form, and decompressed for use in a fraction of a second per scene.

In this paper, we present an efficient way of implementing OpenGL ES 1.1 3D graphics API library for the environments with hardware-supported OpenGL facility, typically as desktop PCs. Although OpenGL ES was started from the existing OpenGL features, it rapidly became the standard 3D graphics library customized for embedded systems through introducing fixed-point arithmetic operations, buffer management with fixed-point data type supports, completely new texture mapping functions and others. Currently, it is the official 3D graphics API for Google Android, Apple iPhone, Sony PlayStation3, etc. In this paper, we achieved improvements on the arithmetic operations for the fixed-point number representation, which is the most characteristic data type for OpenGL ES 1.1. For the conversion of fixed-point data types to the floating-point number representations for the underlying OpenGL, we show the way of efficient conversion processes even with satisfying OpenGL ES standard requirements. We also introduced a specialized memory management scheme to manage the converted data from the buffer containing fixed-point numbers. In the case of texture processing, the requirements in both standards are quite different, and thus we used completely new software-implementations. Our final implementation of OpenGL ES library provides all of more than 200 functions in the standard specification and passed its conformance test, to show its compliance with the standard. From the efficiency point of view, we measured its execution times for several OpenGL ES-specific application programs and achieved remarkable improvements.

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.

Query co-processing on graphics processors (GPUs) has become an effective means to improve the performance of main memory databases. However, this co-processing requires the data transfer between the main memory and the GPU memory via a low-bandwidth PCI-E bus. The overhead of such data transfer becomes an important factor, even a bottleneck, for query co-processing performance on the GPU. In this paper, we propose to use compression to alleviate this performance problem. Specifically, we implement nine lightweight compression schemes on the GPU and further study the combinations of these schemes for a better compression ratio. We design a compression planner to find the optimal combination. Our experiments demonstrate that the GPU-based compression and decompression achieved a processing speed up to 45 and 56 GB/s respectively. Using partial decompression, we were able to significantly improve GPU-based query co-processing performance. As a side product, we have integrated our GPU-based compression into MonetDB, an open source column-oriented DBMS, and demonstrated the feasibility of offloading compression and decompression to the GPU.

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.

The use of high dynamic range (HDR) textures in real-time graphics applications can increase realism and provide a more vivid experience. However, the increased bandwidth and storage requirements for uncompressed HDR data can become a major bottleneck. Hence, several recent algorithms for HDR texture compression have been proposed. In this paper, we discuss several practical issues one has to confront in order to develop and implement HDR texture compression schemes. These include improved texture filtering and efficient offline compression. For compression, we describe how Procrustes analysis can be used to quickly match a predefined template shape against chrominance data. To reduce the cost of HDR texture filtering, we perform filtering prior to the color transformation, and use a simple trick to reduce the incurred errors. We also introduce a number of novel compression modes, which can be combined with existing compression schemes, or used on their own. (Less)

This paper proposes a hardware architecture for the newly introduced Better Portable Graphics (BPG) compression algorithm. Since its introduction in 1987, the Joint Photographic Experts Group (JPEG) graphics format has been the de facto choice for image compression. However, the new compression technique BPG outperforms JPEG in terms of compression quality and size of the compressed file. The objective of this paper is to present a hardware architecture for enhanced real time compression of the image. The complexity of the BPG encoder library is reduced by using hardware compression wherever possible over software compression because of the real time requirements, possibly in embedded systems with low latency requirements. BPG compression is based on the High Efficiency Video Coding (HEVC), which is considered a major advance in compression techniques. In this paper, only image compression is considered. The proposed architecture is prototyped in Matlab/Simulink. The experimental results prove that the visual quality of BPG compression is higher than that of JPEG with equal or reduced file size. To the best of the authors' knowledge, this is the first ever proposed hardware architecture for BPG compression.

In every computer graphics oriented application today, it is a common practice to texture 3D models as a way to obtain realistic material. As part of this process, mesh texturing, deformation, and visualization are all key parts of the computer graphics field. This PhD dissertation was completed in the context of these three important and related fields in computer graphics. The article presents techniques that improve on existing state-of-the-art approaches related to continuity and interpolation in texture space (texturing), object space (deformation), and screen space (rendering)

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.

This paper features an advanced implementation of the X-ray rendering algorithm that harnesses the giant computing power of the current commodity graphics processors to accelerate the generation of high resolution digitally reconstructed radiographs (DRRs). The presented pipeline exploits the latest features of NVIDIA Graphics Processing Unit (GPU) architectures, mainly bindless texture objects and dynamic parallelism. The rendering throughput is substantially improved by exploiting the interoperability mechanisms between CUDA and OpenGL. The benchmarks of our optimized rendering pipeline reflect its capability of generating DRRs with resolutions of 2048(2) and 4096(2) at interactive and semi interactive frame-rates using an NVIDIA GeForce 970 GTX device.

We present a texture data compression technique for transmission of volumetric weather radar data. High resolution volume rendering of such time varying volumetric data sets requires large size and number of texture files. In case of WebGL based volume rendering on web browsers, performance problems occur due to the latencies associated with loading of 3D assets, especially transmission of huge texture files. Existing compression technique can help to solve this problem. The most relevant work is S3TC texture compression. As an advanced compression scheme, we combine the S3TC compression method with efficient encoding of volume data in the RGBA channels of an image followed by DEFLATE compression to further reduce the file size. We show how this optimized scheme with X3D/X3DOM extensions fits into our weather data visualization application requirements and provide experimental results.

We present a novel compression scheme for high dynamic range textures, targeted for hardware implementation. Our method encodes images at a constant 8 bits per pixel, for a compression ratio of 6:1. We demonstrate that our method achieves good visual fidelity, surpassing DXTC texture compression of RGBE data which is the most practical method on existing graphics hardware. The decoding logic for our method is simple enough to be implemented as part of the texture fetch unit in graphics hardware.

Mobile devices have evolved to a point where interactive 3D graphics is becoming feasible. The first standardized 3D programming interfaces for mobile devices - OpenGL ES for native C/C++ and Mobile 3D Graphics (M3G) for Java applications - are now available to hardware vendors and application developers. The interfaces complement rather than compete with each other and can share the same underlying rendering engine, whether implemented in hardware or software. Three-dimensional graphics on mobile devices is still about converting descriptions of geometry, material, and illumination into pixels shown on a raster display, using the same fundamental algorithms as elsewhere. However, mobile devices' limited capabilities must be reflected in the realizations of those algorithms, as well as in the overall graphics system design. In this article, we describe a design that attempts to take on that challenge, consisting of OpenGL ES, a low-level API, and M3G (also known as JSR-184), a high-level API for Java. We describe how the two interfaces relate to each other and existing graphics architectures on the desktop, and how they attempt to provide optimal features and performance across the whole gamut of different devices. OpenGL ES and M3G, as well as our presentation of them in this article, derive from a long tradition of graphics systems design.