Image compression

This paper addresses an image prediction problem focused on images with no identifiable objects. In it, we present several approaches to predict the next image of a given sequence, when the image lacks the well-defined objects, such as meteorological maps or satellite imagery. In these images no clear borders are present, and any object candidate moves, changes, appears and disappears in any image. Nevertheless, this evolution, though unrestricted, is gradual and, hence, prediction looks feasible. One of the approaches presented here, based on a spatio-temporal autoregressive (STAR) model, offers good results for these kinds of images. The main contribution of this paper is to adapt spatio-temporal models to an image prediction problem. As a byproduct of this research, we have achieved a new image compression method, suitable for images without defined shapes.

JPEG2000 is an international standard for still images intended to overcome the shortcomings of the existing JPEG standard. Compared to JPEG image compression techniques, JPEG2000 standard has not only better not only has better compression ratios, but it also offers some exciting features. As it's hard to meet the real-time requirement of image compression systems by software, it is necessary to implement compression system by hardware. The MQ decoder of the JPEG2000 standard is an important bottleneck for real-time applications. In order to meet the real-time requirement we propose in this paper a novel architecture for a MQ decoder with high throughput which is comparable to that of other architectures and suitable for chip implementation. This architecture has been implemented in VHDL hardware description language and synthesized using Xilinx's and Altera's design flows respectively ISE 13.1 and Quartus. The implementation results show that the design operates at 439.5 MHz when implemented on Virtex-6 and the estimated frame rate at this frequency is 63.24 frames per second (FPS). On Stratix III device, the design operates at 214.4 MHz and the hardware cost is very low. Hardware overhead is minimized to a great extent because the structure of the probability estimation table (PET) is replaced by a small PET ROM. The memory bits used in the architecture are reduced significantly. The use of a dedicated probability estimation table decreases the internal memory.

The field of CCTV surveillance is topical and widely used in many different applications. The fundamental part of the CCTV system is a reliable image evaluation by a human observer, whose effectiveness is influenced by many variables. Optimization of the effectiveness is a multidimensional problem related to both technical and human characteristics. In many applied systems, the overall performance is affected by a real performance of technical system (image compression, channel transmission, etc.). On the other hand these technical systems have different optimization criteria than a typical video system. TNO Defence, Security and Safety (formerly the TNO Physics and Electronics Laboratory) initiated a project in which the influence of several technical variables on the operator's effectiveness was studied. The whole project was carried out in close co-operation with the Czech Technical University in Prague (CTU). The paper demonstrates and summarizes selected experimental results and evaluated dependencies.

Images form integral part of interactive services broadcasted in digital television. Natural images compression is always trade-off between visual quality and file size. Annoying artifacts arise in highly compressed images. The scope of this article is to investigate the possibilities of image compression enhancement for interactive services. The aim is to decrease size of the image file in such way that the following post-processing compensates the drop of the visual quality. A low-complex post-processing technique is evaluated. Proposed solution is suitable for decreasing downloading time of the images in digital video broadcasting services.

At the present time, block-transform coding is probably the most popular approach for image compression. For this approach, the compressed images are decoded using only the transmitted transform data. We formulate image decoding as an image recovery problem. According to this approach, the decoded image is reconstructed using not only the transmitted data but, in addition, the prior knowledge that images before compression do not display between-block discontinuities. A spatially adaptive image recovery algorithm is proposed based on the theory of projections onto convex sets. Apart from the data constraint set, this algorithm uses another new constraint set that enforces between-block smoothness. The novelty of this set is that it captures both the local statistical properties of the image and the human perceptual characteristics. A simplified spatially adaptive recovery algorithm is also proposed, and the analysis of its computational complexity is presented. Numerical experiments are shown that demonstrate that the proposed algorithms work better than both the JPEG deblocking recommendation and our previous projection-based image decoding approach

In today’s world of electronic revolution digital images play an important role in consumer electronics. Digital images are stored in different formats, each format having its own set of characteristics making it suitable for any specific area of application. This paper analyzes different image file formats which gives an idea about different file formats with their advantages, disadvantages and analyzes their specific areas of application. Examples analyzing certain images, their file formats and their visible properties are also included

Compressing an image is significantly different than compressing raw binary data. compressing images is used by this different compression algorithm. Wavelet transforms used in Image compression methods to provide high compression rates while maintaining good image quality. Discrete Wavelet Transform (DWT) is one of the most common methods used in signal and image compression .It is very powerful compared to other transform because its ability to represent any type of signals both in time and frequency domain simultaneously. In this paper, we will moot the use of Wavelet Based Image compression algorithm- Embedded Zerotree Wavelet (EZW). We will obtain a bit stream with increasing accuracy from ezw algorithm because of basing on progressive encoding to compress an image into . All the numerical results were done by using matlab coding and the numerical analysis of this algorithm is carried out by sizing Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR) for standard Lena Image .Experimental results beam that the method is fast, robust and efficient enough to implement it in still and complex images with significant image compression.

A compressão de dados é um dos factores que mais contribuiu para o grande crescimento das tecnologias da informação e da comunicação. Sem compressão, a maioria dos produtos tecnológicos de consumo e entretenimento, que são hoje banais, nunca teria chegado a existir, destacando-se, entre muitos outros, o DVD, as câmaras fotográficas digitais, os leitores de MP3, o YouTube e o streaming de vídeo, as redes sem fios e a televisão digital. De facto, as tecnologias de compressão multimédia permitem representar a informação de uma forma mais eficiente, reduzindo os grandes volumes de espaço de armazenamento que ocupa e, portanto, a largura de banda que consome para se transmitir nas redes e na Internet. Este livro tem como objectivo principal apresentar uma introdução fundamentada, clara, acessível e abrangente aos conceitos e tecnologias relacionadas com métodos, algoritmos e normas internacionais de compressão de informação multimédia. Ao longo do livro são abordados, entre outros, os seguintes temas: Conceitos e normas sobre compressão de informação multimédia, incluindo texto, imagens, áudio e vídeo; Entropia e algoritmos de compressão sem perdas: Shannon-Fano, Huffman, Aritmética e LZW; Psicoacústica, métodos e normas de compressão de áudio, incluindo voz (ADPCM, vocoders) e áudio de alta-fidelidade (transformadas, MP3, AAC, MP4 e Dolby AC-3); Teoria rate-distortion, métodos de compressão de imagem (DPCM, transformadas DCT, waveletet Haar, quantificadores lineares e não-lineares); Normas internacionais JPEG e JPEG2000; Compressão de vídeo, redundância espacial e temporal, quantificação de vídeo para aplicações multimédia e de videoconferência; Normas MPEG.

Image Compression is a wide area. Compression of Images increasing because the raw images need large amounts of disk space seems to be a big disadvantage during transmission & storage. Even though there are so many compression technique already present a better technique which is faster, memory efficient and simple surely suit the requirements of the user. The work is to explain use of different compression techniques more suitable for particular data compression algorithm based on compression ratio, Bit per Pixel, Mean Square Error and Peak Signal to Noise Ratio. Using all four parameters image compression works and gives compressed image as an output.

It is well known that the images, often used in variety of computer applications, are difficult to store and transmit. One possible solution to overcome this problem is to use a data compression technique where an image is viewed as a matrix and then the operations are performed on the matrix. Image compression is achieved by using Singular Value Decomposition (SVD) technique on the image matrix. The advantage of using the SVD is the property of energy compaction and its ability to adapt to the local statistical variations of an image. Further, the SVD can be performed on any arbitrary, square, reversible and non reversible matrix of m x n size. In this paper, SVD is utilized to compress and reduce the storage space of an image. In addition, the paper investigates the effect of rank in SVD decomposition to measure the quality in terms of MSE and PSNR.

Image compression is an implementation of the data compression which encodes actual image with some bits. The purpose of the image compression is to decrease the redundancy and irrelevance of image data to be capable to record or send data in an effective form. Hence the image compression decreases the time of transmit in the network and raise the transmission speed. In Lossless technique of image compression, no data get lost while doing the compression.
To solve these types of issues various techniques for the image compression are used. Now questions like how to do image compression and second one is which types of technology is used, may be arises.
For this reason commonly two types’ of approaches are explained called as lossless and the lossy image compression approaches. These techniques are easy in their applications and consume very little memory. An algorithm has also been applied to compress images by using the Huffman encoding techniques.