Intra-picture Coding

Image compression is an important task for image transmission and storage. Thus far many compression techniques have been developed, such as transform image coding, predictive image coding, Vector Quantisation (VQ) and so on. Of these compression techniques, the techniques based on transform coding and VQ have received considerable attention. Recently, hybrid schemes for effective image compression have gained enormous popularity among researchers. In this paper, we analyse a hybrid scheme combining Kohonen’s Self-Organising Feature Map (SOFM)-based VQ coding and Set Partitioning In Hierarchical Trees (SPIHT) coding for compression of images (Rawat and Meher, 2009). The reconstructed image quality achieved after decoding depends upon the wavelet used in SPIHT. The effectiveness of the scheme for various orthogonal and biorthogonal wavelets for the hybrid scheme is tested in terms of Peak Signal-to-Noise Ratio (PSNR) and Visual Information Fidelity (VIF).

In this paper, the fractal function is used for the image representation and compression. By introducmg a searching strategyinto the fractal-function-based image coding, the image representation accuracy is improved by 10%. A regional adaptiveresolution-based fractal block coding is formed by combining the encoding and the decoding processes together with aquadtree structure to perform image compression. It is shown by the experiments that the image compression ratio isincreased up to 1 2 without a great loss of visual quality.Keywords: Image coding; image compression; fractal function. 1. INTRODUCTION AND THEORETICAL FOUNDATION Fractal has been stimulating great interests since John Hutchinson introduced the theory of iterated function system(IFS) [1] to model collections of contractive transformations in a metric space as dynamical systems. After the work of M.Barnsley and A. E. Jacquin [2]-[3] in using the partitioned iterated function system (PIFS) to do fractal-based imagecompression, many works have been done either to improve the compression ratio or to reduce the encoding time. F.Dudbridge[4]-[5] developed a method which is quite different from the traditional fractal (TF) image coding. He proposedusing a new fractal function (FF) for the image representation.Before the concept of fractal function is introduced the following preliminaries are needed: (1) w1 ,

This paper proposes joint-probability-based adaptive Golomb coding (JPBAGC) to improve the performances of the Golomb family of codes, including Golomb coding (GC), Golomb–Rice coding (GRC), exp-Golomb coding (EGC), and hybrid Golomb coding (HGC), for image compression. The Golomb family of codes is ideally suited to the processing of data with geometric distribution. Since it does not require a coding table, it has higher coding efficiency than Huffman coding. In this paper, we find that there are many situations in which the probability distribution of data is not only geometric, but also depends on the probability distribution of the other data. Accordingly, we used the joint probability of generalizing the Golomb family of codes and exploiting the dependence between neighboring image data. The proposed JPBAGC improves the efficiency of many image and video compression standards, such as the joint photographic experts group (JPEG) compression scheme and the H.264-intra JPEG-based image coding system. Simulation results demonstrate the superior coding efficiency of the proposed scheme over those of Huffman coding, GC, GRC, EGC, and HGC.

Fractal image coding is a novel technique for still image compression. Compared with the distance between the range block and the matching domain block, setting of the initial threshold value is one of the most difficult problems in Fisher Quadtree-based fractal image coding. In this paper, a novel fractal image coding based on Quadtree partition of the adaptive threshold value is proposed. Considering the input image feature fully, we put forward the computation derivation process of the adaptive threshold value progressively and declare that the adaptive threshold value has the direct proportion with the variance of the current range block. Experimental results show that compared with Fisher Quadtree-based fractal coding for the same image, the proposed coding scheme obtains better performance including the improved quality of the decoded image, shorter compression time and higher compression ratio.

The growing demand for visual telecommunication services over the last decade has greatly increased the need for efficient image coding and compression schemes. The work presented in this thesis examines several aspects of the problem of coding and compressing the various image and video-based services which are likely to utilize Broadband Integrated Services Digital Networks (BISDNs) in the future. This research has two major thrusts. The first is the development of a general-purpose, high-performance and high-quality image coding and compression scheme for these broadband-based visual services. The second is the development of an accurate traffic source modelling scheme for the Variable BitRate (VBR) packet video traffic produced by the proposed coding scheme. The proposed high-performance visual coding and compression scheme combines both statistical and psychovisual coding techniques to produce an optimum scheme which removes both statistical and psychovisual redundancies from images in the coding process. When used to encode four standard 512x512x8-bit test images, this scheme results in sub-distortion compression ratios of up to 27:1 without the use of any form of interframe coding. An efficient and flexible Asynchronous Transfer Mode (ATM) cell packaging scheme is also developed which allows the production of either 'priority' or 'non-priority' cell streams suitable for transmission over broadband networks. Three suitable VBR packet video traffic source models are developed during this study. Each of these sources reproduces the 'low-level' cell generation process and switches between different 'modes' of cell generation in order to capture the inherent `burstiness' and 'variability' of typical VBR packet video traffic streams. However, the way in which this cell production process is modelled, as well as the traffic levels which can be reproduced, differs for each of these sources. A relatively complex, general-purpose traffic source is proposed which is based on the hidden Markov statistical model and is able to model all levels of VBR traffic (up to 20 Mbps). A simpler 'switched-fractal' source is also proposed as an accurate model for the low (below 5 Mbps) VBR traffic only. The third proposed source is a 'switched-Markov' model which specifically caters for the high (5 to 20 Mbps) levels of VBR traffic. Among these three artificial traffic sources, the characteristics of all levels of traffic produced by the proposed video and image coding scheme are able to be reproduced accurately.

The performance of a vector quantizer can be improved by using a variable-rate code. Three variable-rate vector quantization systems are applied to speech, image, and video sources and compared to standard vector quantization and noiseless variable-rate coding approaches. The systems range from a simple and flexible tree-based vector quantizer to a high-performance, but complex, jointly optimized vector quantizer and noiseless code. The systems provide significant performance improvements for subband speech coding, predictive image coding, and motion-compensated video, but provide only marginal improvements for vector quantization of linear predictive coefficients in speech and direct vector quantization of images. Criteria are suggested for determining when variable-rate vector quantization may provide significant performance improvement over standard approaches.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

In this study, the performance of fractal‐based coding algorithms such as standard fractal coding, quasi‐lossless fractal coding and improved quasi‐lossless fractal coding are evaluated by investigating their ability to compress magnetic resonance images (MRIs) based on compression ratio, peak signal‐to‐noise ratio and encoding time. For this purpose, MRI head scan test sets of 512 × 512 pixels have been used. A novel quasi‐lossless fractal coding scheme, which preserves important feature‐rich portions of the medical image, such as domain blocks and generates the remaining part of the image from it, has been proposed using fractal transformations. One of the biggest tasks in fractal image compression is reduction of encoding computation time. A machine learning‐based model is used for reducing the encoding time and also for improving the performance of the quasi‐lossless fractal coding scheme. The results show a better performance of improved quasi‐lossless fractal compression method. The quasi‐lossless and improved quasi‐lossless fractal coding algorithms are found to outperform standard fractal coding thereby proving the possibility of using fractal‐based image compression algorithms for medical image compression. The proposed algorithm allows significant reduction of encoding time and also improvement in the compression ratio.

Transform coding is commonly used in image processing algorithms to provide high compression ratios, often at the expense of processing time and simplicity of the system. We have recently proposed a pixel value prediction scheme in order to exploit adjacent pixel correlation, providing a low-complexity model for image coding. However, the proposed model was unable to reach high compression ratios retaining high quality of reconstructed image at the same time. In this paper we propose a new segmentation algorithm which further utilizes adjacent pixel correlation, provides higher compression ratios and it is based on application of Hadamard transform coding. Additional compression is provided by using vector quantization for a low number of quantization levels and by simplifying generalized Lloyd’s algorithm where the special attention is paid to determination of optimal partitions for vector quantization, making a fixed quantizer. The proposed method is quite simple and experimental results show that it ensures better or similar rate-distortion ratio for very low bit-rates, comparing to the other similar methods that are based on wavelet or curvelet transform coding and support or core vector machine application. Furthermore, the proposed method requires very low processing time since the proposed quantizers are fixed, much less than the required time for the aforementioned methods that we compare with as well as much less than the time required for fractal image coding. In the end, the appropriate discussion is provided comparing the results with a scheme based on linear prediction and dual-mode quantization.

In this paper, we present an efficient hybrid image compression method. According to Human eye sensitivity, It is not sensitive to gray-scale error, but the human eye is particularly sensitive to the image edge features, through we research strengths and weaknesses of the fractal image coding and SPIHT(set partitioning in hierarchical trees) algorithm, proposed a hybrid algorithm based on SPIHT and Fractal image compression algorithm, it makes full use of landscape features and human visual characteristics, the image is decompounded into low frequency sub-band and high frequency sub-band, then the low frequency sub-band is uses fractal coding, this could make the low-frequency information loss less who is sensitivity to human visual signal and the high frequency sub-band is uses SPIHT coding, so encoding time greatly reduced. Experimental results show that the Hybrid Image Compression Algorithm not only raises the coding efficiency and reconstructed image quality but also reduces the image encoding time.

The increase of the need for image storage and transmission in computer systems has increased the importance of signal and image compression algorithms. The approach involving vector quantization (VQ) relies on the design of a finite set of codes which will substitute the original signal during transmission with a minimal of distortion, taking advantage of the spatial redundancy of image to compress them. Algorithms such as LBG and SOM work in an unsupervised way toward finding a good codebook for a given training data. However, the number of code vectors (N) needed for VQ increases with the vector dimension, and full-search algorithms such as LBG and SOM can lead to large training and coding times. An alternative for reducing the computational complexity is the use of a tree-structured vector quantization algorithm. This paper presents an application of a hierarchical SOM for image compression which reduces the search complexity from O(N) to O(log N), enabling a faster training and image coding. Results are given for conventional SOM, LBG and HSOM, showing the advantage of the proposed method.

In distributed underwater signal processing for area surveillance and sanitization during regional conflicts, it is often necessary to transmit raw imagery data to a remote processing station for detection-report confirmation and more sophisticated automatic target recognition (ATR) processing. Because of the limited bandwidth available for wireless transmission, image compression is of paramount importance. Furthermore, it is equally crucial that image coding algorithms be evaluated according to some meaningful criteria. Instead of assessing the performance of image compression algorithms in terms of peak signal-to-noise ratio (PSNR) or normalized mean-squared error (NMSE), we resort to a more meaningful performance metric that reflects human and operational factors-ATR performance. We develop a novel image compression algorithm that achieves the minimal information state by a combination of subimage-specific transformation, principal component analysis, and vector quantization (VQ). We quantify the performance of image coding by extracting key parameters or features from the original and reconstructed images and by comparing the ATR performances using separate test sets that contain both mines and mine-like clutter. We achieve a compression ratio of up to 57:1 with minimal sacrifice in P/sub D/ and P/sub FA/.

The aim of this study is to hide two binary BMP images in a single BMP grayscale image. The widespread technique in image steganography is to hide one image (stego image) into another (cover image). The proposed novel method is to hide two binary images into one grayscale bitmap cover image. First of all, the proposed technique starts with transforming all grayscale cover image pixels into multiples of five using Five Modulus Method (FMM). Clearly, any modulus of five is either 0, 1, 2, 3, or 4. The transformed FMM cover image could be treated as a good host for carrying data. Obviously, it is known that the pixel value for the binary image is either 0 or 1. Therefore, by concatenating the two binary images, the composite results are 00, 01, 10 and 11. In fact, these concatenated values could be mapped using simple mapping that assigns a positive integer value such as 1 for 00, 2 for 01, 3 for 10 and 4 for 11. Consequently, a new matrix will be constructed that contains a number varying from 1 to 4 only. Fortunately, the four integer values are the same as the previously mentioned reminders of division by 5, hence, adding these four integers to the transformed FMM cover image. On the recipient side, a reverse process will be implemented to extract the two binary images. In terms of PSNR values, the cover image and the two extracted stego images have acceptable PSNR values, which yields that the proposed method is very efficient in information hiding.

With the rapid growth of image transmission over the public networks, image compression and encryption has received increasing attention to reduce the redundancy and ensure security. In this paper, a scheme of image compression and encryption based on vector quantization (VQ) and cross chaotic map has been proposed that simultaneously compresses and encrypts the image based on confusion-diffusion cryptosystem. Extensive security analysis has been performed on the proposed scheme by using key space analysis, key sensitivity analysis, statistical analysis and differential analysis. Results of our analysis show that the proposed scheme has high security, significant savings in computation and good compression performance.

This paper presents a hybrid image compression scheme based on a block based compression algorithm referred to as Vector Quantization (VQ) combined with the Differential Pulse Code Modulation (DPCM) technique. The proposed image compression technique called the PVQ scheme results in enhanced image quality as compared to the standalone VQ. The generated codebooks for the PVQ scheme are more robust for image coding than that of the VQ. This made our system a suitable candidate for developing on chip image sensor with integrated data compression processor. The proposed system was validated through FPGA implementation. The resulting implementation achieved good compression and image quality with moderate system complexity.