Multimedia extensions for DLX processor

Compression of images process is a step in the image processing. It is concerned with the transmission and storage of digitally created images. Fractal coding is a potential image and video compression approach with good reconstruction fidelity and relatively large compression ratios, because of its simplicity and great performance; fractal image compression (FIC) is a particularly popular approach in image compression applications. However, it has a significant disadvantage in the form of a long encoding time. This is because encoding any small bit necessitates a massive similarity search in the original data As a result; the FIC search time is reduced while the quality of the reconstructed images is maintained acceptable level in many introduced paper and other still a study topic in progress. Fractal images are images that are self-similar in that each individual part is the same as the total. This paper will discusses many attempts for more author that working on image and video compression using fractal compression technique based on various approach and with each discuss focuses on the main parameter of compression such compression ratio (CR), peak signal to noise ratio (PSNR) and encoding time, as well as the details of data set the used for testing also writing with each technique to creating fractal video and image compression. Index Terms— FIC, Video compression, parallel processing, Iteration Function System (IFS), Image processing.

Block based transform coding is the most popular technique for image and video compression. However, images compressed using block based algorithms exhibit blocking which is one of the most annoying artifacts in compressed images and video. Many deblocking algorithms have been proposed to improve the quality of the reconstructed image. Although the deblocking operation improves the image appearance by smoothing the block discontinuities, a loss of information also occurs during this process. As a consequence, there is a need to develop methods to reduce the blocking artifacts while minimizing the information loss. We propose a novel fusion based technique to minimize the loss of useful information which may result from the deblocking operation. The loss of key image information can be reduced by fusing a deblocking result and the compressed image.

In modern world more image and video compression technologies improving gradually. But major barrier of the new technologies concepts are repeated from authors. Because authors can’t find in depth of the papers from various technologies. So Survey of Video Compression Techniques very helpful for these types problems in video compression areas. Video compression techniques such as DCT coding, Quantization, Entropy coding, Motion estimation are widely used in video compression techniques. The main focus of this paper is to analyse video compression techniques required for video processing especially to discover how much amount of data to compressed, which techniques is faster and visual quality better and so on. We evaluate the video compression techniques for finding compression ratio in terms of performance, speed and accuracy. We also compare video compression techniques with conventional methods.

From the Publisher: The field of signal recovery, which is relatively mature, has long been associated with a wealth of powerful mathematical techniques such as Bayesian estimation, and the theory of projections onto convex sets (to name just two). This book illustrates, for the first time in a complete volume how these techniques can be brought to bear on the very important problems of image and video compression and transmission.

Providing a thorough theoretical understanding of lossy compression techniques for image, video, speech, and audio compression, this book also covers the key features of each system, as well as practical applications, implementation issues, and design trade-offs. It presents comparisons of multimedia standards in terms of achieving known theoretical limits, whilst common and distinguishing features of the existing standards are explained and related to the background theory. There is detailed coverage of such topics as the H.264 video coding standard, low-complexity code-based vector quantizers, and the Blahut rate-distortion algorithm. Examples based on real multimedia data are also included, plus end-of-chapter problems to test understanding, algorithms that allow the reader to represent speech and audio signals efficiently, and an appendix on the basics of lossless coding. With an excellent balance of theory and practice, this book is ideal for undergraduate and graduate students, and is also a useful reference for practitioners.

Mediaprocessors, such as Philips Trimedia and Hitachi/Equator Technologies MAP, combine the computational power of high-end DSPs with various I/O capabilities in a single programmable chip. due to their programmability, mediaprocessors have greater flexibility than ASICs and other special-purpose hardware. Early mediaprocessors, such as Texas Instruments TMS320C80 since its introduction in 1994, have had limited success due to their difficulty in programming, insufficient computational power, and high cost. Fortunately, several newer mediaprocessors, which are available or under development, are easier to program, are less expensive, and/or have more computational power. However, due to the earlier difficulties and inherent uncertainties in the programmable solutions, mediaprocessor user companies (set makers) are often hesitant in adopting the mediaprocessors in their products. Furthermore, set makers still need to expend a lot of time and manpower in making a successful transition from hardwired to mediaprocessor-based products. Therefore, we introduce the Mediaprocessor (MP) Consortium, which aims to remove the barrier to the widespread use of programmable mediaprocessors. Through publications, web site, training courses, software libraries, and objective evaluations of mediaprocessors, the MP Consortium can increase the awareness of the benefits of mediaprocessors over ASICs, make the transition to mediaprocessor-based products easier for set makers, and help them attain full advantage of using mediaprocessors.

Uncompressed form of the digital images are needed a very large storage capacity amount, as a consequence requires large communication bandwidth for data transmission over the network. Image compression techniques not only minimize the image storage space but also preserve the quality of image. This paper reveal image compression technique which uses distinct image coding scheme based on wavelet transform that combined effective types of compression algorithms for further compression. EZW and SPIHT algorithms are types of significant compression techniques that obtainable for lossy image compression algorithms. The EZW coding is a worthwhile and simple efficient algorithm. SPIHT is an most powerful technique that utilize for image compression depend on the concept of coding set of wavelet coefficients as zero trees. The proposed compression algorithm that combined dual image compression techniques (DICT) invest an excellent features from each methods, which then produce promising technique for still image compression through minimize bits number that required to represent the input image, to the degree allowed without significant impact on quality of reconstructed image.
 The experimental results present that DICT will improve the image compression efficiency between 8 to 24%, and will result high performance of metrics values.

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The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames difference approaches that concentrated on the calculation of frame near distance (difference between frames). The selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this paper, many videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained.

The main aim of image compression is to represent an image with minimum number of bits, so that the storage requirement can be reduced, thereby increasing the transmission rate without losing significant features of the image. The compression ratio is affected by noise, as it degrades the correlation between pixels. During capture, processing or transmission of the image, noise can occur. The noise possibly can be independent of or dependent on image content. On lossy image compression algorithms, the effect of noise has been studied in this paper. In order to study the effect of noise, the original images act as a reference to the reconstructed images. The reconstructed images are compared with the original images in terms of PSNR. The proposed image encoder integrates the features of curvelet transform with both radial basis function neural network (RBFNN) and back-propagation neural network (BPNN) separately and results are presented for both the cases. The case studies which consider images with noise prove the superiority of the techniques in terms of highly acceptable PSNR values. The merits of the proposed technique are further exemplified by comparing the results with those of JPEG and JPEG 2000.

Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF's, using simulation, it is possible to explore a high-quality captured LF's properties. The immediate concerns following the LF capture are its storage and processing time. A rich LF occupies a large chunk of memory ---order of multiple gigabytes per LF---. Also, most feature extraction techniques associated with LF postprocessing involve multi-dimensional integration that requires access to the whole LF and is usually time-consuming. Recent advancements in computer processing units made it possible to simulate realistic images using physical-based rendering software. In this work, at first, a transformation function is proposed for building a camera array (CA) to capture the same portion of LF from a scene that a standard plenoptic camera (SPC) can acquire. Using this transformation, LF simulation with similar properties as a plenoptic camera will become trivial in any rendering software. Artificial intelligence (AI) and machine learning (ML) algorithms ---when deployed on the new generation of GPUs--- are faster than ever. It is possible to generate and train large networks with millions of trainable parameters to learn very complex features. Here, residual convolutional neural network (RCNN) structures are employed to build complex networks for compression and feature extraction from an LF. By combining state-of-the-art image compression and RCNN, I have created a compression pipeline. The proposed pipeline's bit per pixel (bpp) ratio is 0.0047 on average. I show that with a 1% compression time cost and 18x speedup for decompression, our methods reconstructed LFs have better structural similarity index metric (SSIM) and comparable peak signal-to-noise ratio (PSNR) compared to the state-of-the-art video compression techniques used to compress LFs. In the end, using RCNN, I created a network called RefNet, for extracting a group of 16 refocused images from a raw LF. The training parameters of the 16 LFs are set to (\alpha=0.125, 0.250, 0.375, ..., 2.0) for training. I show that RefNet is 134x faster than the state-of-the-art refocusing technique. The RefNet is also superior in color prediction compared to the state-of-the-art

Security and privacy issues of the transmitted data have become an important concern in multimedia technology. In this paper, we propose two computationally efficient and secure video in-compression encryption models. Both models use adaptive lossy image compression (ALIC) technique (El-said et al., 2010) for video compression and optimised multiple Huffman tables (OMHT) technique (El-said et al., 2011) for video encryption. We achieve computational efficiency by using the proposed ALIC technique that exploits the frequently occurring patterns in the DCT coefficients of the video data. Computational complexity of the encryption is made proportional to the number of DCT transmitted coefficients in each video frame. The proposed video encryption models produce an encrypted coded sequence with an improved quality for the high compression ratios when compared to the existing techniques. The performances of the proposed models are compared with those of the existing techniques with respect not only to their compression performance but also their security level. The simulation study for the two models over mobile communication system proves that the proposed models perform well in the presence of transmission errors.

With the rapid growth of wireless sensor network technologies and the availability of less expensive hardware such as complementary metal oxide semiconductor cameras and microphones has encourage the development of wireless multimedia sensor network. Some application areas for wireless multimedia sensor network are health care monitoring, traffic monitoring, environment monitoring and wildlife monitoring. The constraints of sensors are limited processing capability, limited storage and energy. Energy consumed by sensor during image transmission is very high due to large size of images. Hence, networks lifetime is decreased rapidly. A numbers of techniques have been proposed to enhance the network lifetime. Image compression techniques are one of them. This paper describes the various image compression techniques, which can be used to aid energy efficient wireless multimedia sensor network. Furthermore, we have compared various image compression techniques, so one can find out suitable compression technique for their application.

Recent advances in computer hardware and software can enable individual instructors to easily create and deliver prerecorded lectures via the World Wide Web. Using new video and audio compression techniques, an entire semester of lectures can be stored on less than 2 gigabytes of disk space and delivered over the Internet on demand. A methodology for quickly generating effective lectures at relatively low video frame rates is described, and examples of the technique are provided on the Web.

Image compression is highly essential for efficient transmission and storage of images in the field of communication engineering, bio-medical applications. Also, the compression technology is of special interest for the fast transmission and real-time processing on the internet. For reduced form and less capacity, the area of research growing day by day. The objective of image compression is to find a new representation in which pixels are less correlated, but with the original contents. In this paper, the existing as well as new algorithms are applied for compression for evaluation. The results have been compared for both techniques. On the basis of evaluating and analyzing the image compression techniques it presents the VHDL implementation of low complexity 2D-DWT approach applied to image compression. The decompression has to invert the transformations applied by the compression to the image data. When using the wavelet transform it is possible to exploit the unique properties of the wavelet coefficients to efficiently encode them.