Latest Research Papers On Image Compression

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image compression and decompression




A Gaussian derivative based version of JPEG for image compression and decompression
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ABSTRACT The compression and decompression of continuous-tone images is important in document management and transmission systems. This paper considers an alternative image representation scheme, based on Gaussian derivatives, to the standard discrete

Image compression and decompression using adaptive interpolation
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ABSTRACT A simple and fast lossy compression and decompression algorithm for digital images is proposed. The method offers varying compression ratios (depending on dimensions of the image) and the acquired decompressed image is close to the original

JPEG Image Compression and Decompression with Modeling of DCT Coefficients on the Texas Instrument Video Processing Board TMS320DM6437
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ABSTRACT Image compression has become one of the most important disciplines in digital electronics because of the ever-growing popularity and usage of the internet and multimedia systems combined with the high requirements of the bandwidth and storage space.

N-Square Approach For Lossless Image Compression And Decompression
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ABSTRACT There are several lossy and lossless coding techniques developed all through the last two decades. Although very high compression can be achieved with lossy compression techniques, they are deficient in obtaining the original image. While lossless compression

Image Compression and Decompression using nested Inverse Fourier Transform and Fast Fourier Transform
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ABSTRACT Image compression is the reduction or elimination of redundancy in image data representation in order to achieve savings in storage and communication. In this paper we propose a method for image compression and decompression using nested IFFT and FFT

Image Compression and Decompression using Spiht
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ABSTRACT The main aim of the project is to perform image compression and decompression using set partition in hierarchical trees method (SPIHT). We use matlab 6.5 simulation to perform this task. SPIHT exploits properties that are present in a wide variety of images.

Digital Image Compression and Decompression Using Three Different Transforms and Comparison of Their Performance.
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Data compression is an important tool in digital image processing to reduce the burden on the storage and transmission systems. The basic idea of data compression is to reduce the number of the image pixel elements directly, say by sampling, or by using transforms and

error diffused image for multi thread concept using lossless compression



Interframe wavelet coding motion picture representation for universal scalability
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Cover image Cover image. Interframe wavelet coding motion picture representation for universal scalability. In principle, the highpass frame shows the same behavior as a prediction error frame without motion compensation. Full-size image (59 K) Full-size image (59 K) Fig. 10.

Image acquisition and processing with LabVIEW
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and Collimated on Axis Lights (COAL).43 2.4.8 Square Continuous Diffuse Illuminators (SCDI Often the number of colors used to represent the image can be dramatically decreased (some vision routines require images to be

Out-of-Core Multi-Resolution Volume Rendering of Large Data Sets
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L* for lightness of the color, L*=0 for black and L* =100 for diffuse white. OpenCL also supports two- dimensional and three-dimensional image buffers with support for hard- ware based interpolation. The resulting error is pre- calculated and stored as meta-data. 20 Page 26.

VBS PURVANCHAL UNIVERSITY JAUNPUR
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Lines and Surfaces: Back Face Detection algorithm, Depth buffer method, A- buffer method, Scan line method, basic illumination models – Ambient light, Diffuse reflection, Specular Error Detection Recovery: Lexical Phase errors, syntactic phase errors semantic errors.

PROPOSED SYLLABUS
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Real Time System ECS-083 Embedded Systems EIT-081 Digital Image Processing EIT buffer method, Scan line method, basic illumination models – Ambient light, Diffuse reflection, Specular Link Layer Elementary Data Link Protocols, Sliding Window protocols, Error Handling

White Paper Stream Processing: Enabling the new generation of easy to use, high-performance DSPs
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Floyd-Steinberg Error Diffusion eg MCTF, de-interlacing and image stabilization display processing, eg scaling, tiling and OSD image sensor pipeline adaptive compression, eg higher resolution and lossless compression of tracked objects digital PTZ, eg multi-window and

A novel image reconstruction software for optical/infrared interferometry
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for rapid rotators, complicated spot patterns on rotating RS CVn binaries, and diffuse disk emission Following the OIFITS standard, the data points are supposed to have independent Gaussian errors. to evaluate how reliable the image features are by associating error bars to

Parallel Animated Image File Generation
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the computation and visualization algorithms will use the same data structures, which can lead to errors. when they transform into each other and how the substrates spread (diffuse) over said This (diffusion) is achieved by averaging the concentrations of the substrate for all the

Complexity Reduction In H. 264 Encoder Using Open Multiprocessing
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The frame is processed in units of a macroblock corresponding to 16x16 pixels in the original image. Each macroblock is encoded in intra or inter mode. The prediction block is then created by each of the predictions. The Sum of Absolute Errors (SAE) error. reconstruction quality in diagnostically relevant regions is essential, while other regions can contain more errors. Despite the fact that this leads to a higher error at the known pixel also supports colour images with a straightforward extension to vector-valued diffusion in RGB

COMPLEXITY REDUCTION IN H. 264 ENCODER USING
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The frame is processed in units of a macroblock corresponding to 16x16 pixels in the original image. Each macroblock is encoded in intra or inter mode. The prediction block is then created by each of the predictions. The Sum of Absolute Errors (SAE) error.

GROMACS: fast, flexible, and free
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with Ewald summation (see below), the Coulomb interaction is modified with an error function. mostly increase) during equilibration, during conformational changes, and as a result of errors in the of the system, for example, to restrain alpha carbons or calculate the diffusion of a

Multi-modal molecular diffuse optical tomography system for small animal imaging
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volumetric and spatially resolved BLI via BLT alongside spectral diffuse optical tomography optical components nor the animal have to move between images and in by utilizing absorption measurement by integrated photo-acoustic tomography, FMT image reconstruction could

State-of-the-Art in Compressed GPU-Based Direct Volume Rendering
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to be performed in real-time while satisfying a certain application-defined image quality error as unacceptable [FY94], unless the compression error is within the limit of acquisition errors, or a be introduced at any stage of the visualization pipeline shows that the error tolerance is

Building bridges between human vision and electronic imaging: a ten-year retrospective
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Jennifer's talk focused on color rendering across devices using error diffusion with "web-safe" colors. median of the painting, paper on perceiving depth in 2-D artistic images. The first session devoted to image analysis also appeared in 1989, and this topic has

Context-based polynomial extrapolation and slackened synchronization for fast multi-core simulation using FMI
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a trade-off must be found between accept- able simulation errors, thanks to enhance- ments will consider communication step size control, for which the error analysis and

Robust and flexible hardware implementation of ITU-G4
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The power dissipation is 546.61mW and the compression time for an error diffused image is 203ms. of implementing ITU-G4 in VHDL is probably to eliminate the small errors that show be to simulate the design with every possible image in order to reach an error probability of 0

Bottlenecks in data preparation flow for multi-beam direct write
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Since all that the conversion step does is to reorder the pixels, no errors are introduced in this step. Each pixel then needs to be further processed to take care of issues such as dithering and error quantization and diffusion.

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