Vector Quantization-Fractal Image Coding Algorithm Based on Delaunay Triangulation

We describe a novel adaptive wavelet-based compression scheme for images which takes advantage of image redundancy across scales. As with standard wavelet transform coders, our compressed image representation consists of a set of quantized wavelet coefficients and quantized wavelet subtrees. Instead of having a fixed subtree codebook, however, we construct a codebook from the image being compressed. Subtrees are quantized to contracted isometries of coarser scale subtrees. This codebook drawn from the image is effective for quantizing locally smooth regions and locally straight edges. We prove that this self- quantization enables us to recover the fine scale wavelet coefficients of an image given its coarse scale coefficients. We show that this self-quantization algorithm is equivalent to a fractal image compression scheme when the wavelet basis is the Haar basis. The wavelet framework greatly simplifies the analysis of fractal compression schemes and places fractal compression in the context of existing wavelet subtree coding schemes. We obtain a simple convergence proof which strengthens existing fractal compression results, we describe a new reconstruction algorithm which requires O(N) operations for an N pixel image, and we derive an improved means of estimating the error incurred in decoding fractal compressed images. We show the effect of self-quantization for a test image.

Adaptive Delaunay triangulation with object-oriented programming for crack propagation analysis

Images are compressed using lossy and lossless compression schemes to utilize the Bandwidth in an effective way and also to provide enough space for storing voluminous data. Fractal Image Compression (FIC), is one such lossy compression scheme based on contractive mapping theorem. Affine transforms are being employed in Fractal Image compression to map the range blocks and domain blocks incorporating the property of self similarity in the images. As there is a need to enhance the performance measures like compression ratio (CR) and peak signal to noise ratio (PSNR), an attempt is made in this research paper to analyze FIC using Quad-tree Partitioning technique and Embedded Block Coding Optimization Truncation (EBCOT) technique for Medical Images. Here in this paper FIC with EBCOT is being implemented on Medical Images like CT of Bone and MR Images of Brain and the Performance measures like CR, PSNR at different Threshold Values were measured. Mat lab simulated results for these Performance Measures showed that the PSNR and CR is high when FIC is performed along with EBCOT Encoding.

In this paper, a kind of self-similar coefficient (SSC) of the range blocks in an image is defined in Fractal Image Compression scheme. It also is proved that the range blocks in fractal images possess short distance piecewise self- similarity (SDPS). A novel edge detect method is proposed based on SSC and SDPS, and the result show that this method can be used to extract edge of fractal image effectively.

The Hutchinson metric is a natural measure of the discrepancy between two images for use in fractal image processing. A neural network is described which can quickly calculate this metric. By combining this with the architecture previously described by the author for implementing the Markov operator of an iterated function system (IFS) on a neural network, a fast method is obtained for determining the distance between a target image and the invariant measure of a trial IFS. This has obvious applications to Barnsley's fractal image compression scheme.

Joint image compression and encryption using a novel column-wise scanning and optimization algorithm

Abstract. Lagrangian cloud models (LCMs) are increasingly used in the cloud physics community. They not only enable a very detailed representation of cloud microphysics but also lack numerical errors typical for most other models. However, insufficient statistics, caused by an inadequate number of Lagrangian particles to represent cloud microphysical processes, can limit the applicability and validity of this approach. This study presents the first use of a splitting and merging algorithm designed to improve the warm cloud precipitation process by deliberately increasing or decreasing the number of Lagrangian particles under appropriate conditions. This new approach and the details of how splitting is executed are evaluated in box and single-cloud simulations, as well as a shallow cumulus test case. The results indicate that splitting is essential for a proper representation of the precipitation process. Moreover, the details of the splitting method (i.e., identifying the appropriate conditions) become insignificant for larger model domains as long as a sufficiently large number of Lagrangian particles is produced by the algorithm. The accompanying merging algorithm is essential to constrict the number of Lagrangian particles in order to maintain the computational performance of the model. Overall, splitting and merging do not affect the life cycle and domain-averaged macroscopic properties of the simulated clouds. This new approach is a useful addition to all LCMs since it is able to significantly increase the number of Lagrangian particles in appropriate regions of the clouds, while maintaining a computationally feasible total number of Lagrangian particles in the entire model domain.

In this paper, we propose a new Light Field Image (LFI) compression scheme based on Multiview Video plus Depth (MVD) coding architecture. Through LF function analysis, we preliminarily estimate depth map according to the concept of Epipolar Plane Image (EPI). Such a rough estimation causes some error pixels within initial depth map, so we design a weighting mean filter to smooth the inaccurate region. The final estimated depth maps can be encoded by MVD coding standard jointly with a small number of viewpoint images in LFI, so as to improve compression efficiency of LFI. Ultimately, massive experiments are conducted on 4 LFIs to verify the effectiveness of the proposed compression scheme. The simulated results demonstrate that our LFI compression scheme can achieve a high LFI compression performance and outperform the-state-of-art coding solution.

Color image compression and encryption scheme based on compressive sensing and double random encryption strategy

In this paper, we propose novel progressive scalable region of interest (ROI) image compression schemes with rate, distortion, and complexity trade-off based on vector quantization. The proposed schemes are unbalanced in the sense that the decoder has less computational requirements than the encoder. Moreover, the proposed method provides an interactive tool for fast ROI based browsing from image archives. Residual vector quantization (RVQ) equips the encoder with a multi-resolution apparatus which is useful for rate-distortion trade-of. Having all the advantages of RVQ, jointly sub-optimized RVQ provides a distortion-complexity adjustment. Since the bitstreams associated with the earlier stages of the RVQ encoder are more important for the reconstructed image quality than that of the later stages, an unequal error protection scheme, base on Reed-Solomon codes, has been considered to protect the transmitted bitstream against channel noise.

Requirements for higher resolution remote sensing images lead to rapid increase of data amount in space communications. However, since satellite communications capacity is suffering from great pressure, seeking for more effective compression scheme is supposed to solve existing conflict between tremendous data and limited bandwidth. For this reason, this paper proposes a prior-information-based remote sensing image compression scheme. We firstly utilize prior information contained in historical remote sensing images for incremental image extraction, which is assumed to have removed redundant information possessed both on the satellite and ground. Moreover, Bayesian dictionary serves to sparsely represent the incremental image, generating finite number of representation coefficients in place of numerous pixels. Finally, quantization and encoding schemes are further designed for efficient data transmission. Experimental results show that the proposed scheme is competitive to existing general image compression schemes.

In this paper, a novel detection and recovery system is proposed for image authentication based on two compression schemes: block truncation coding and vector quantization. In order to reduce the overhead in the communication, the sender first compresses an image and then refines the characteristic values from the compressed image. The characteristic values are encrypted to digital signatures using the RSA scheme (proposed by Rivest, Shamir and Adleman). The signatures are embedded into the compressed image and thus the image integrity can be authenticated after transmission. Upon receipt of the compressed image, the receiver extracts the signatures and then decrypts the characteristic values using the sender's public key. Another characteristic value can also be decoded by the image compression scheme. By comparing a few characteristic values, the system determines whether or not the decompressed image is tampered and then outputs a correct characteristic value to rebuild the tampered regions. Furthermore, the proposed scheme authenticates the integrity of the image to the receiver and effectively recovers the tampered image.

This paper proposes a novel image compression scheme based on the local feature descriptor - Scale Invariant Feature Transform (SIFT). The SIFT descriptor characterizes an image region invariantly to scale and rotation. It is used widely in image retrieval. By using SIFT descriptors, our compression scheme is able to make use of external image contents to reduce visual redundancy among images. The proposed encoder compresses an input image by SIFT descriptors rather than pixel values. It separates the SIFT descriptors of the image into two groups, a visual description which is a significantly sub sampled image with key SIFT descriptors embedded and a set of differential SIFT descriptors, to reduce the coding bits. The corresponding decoder generates the SIFT descriptors from the visual description and the differential set. The SIFT descriptors are used in our SIFT-based matching to retrieve the candidate predictive patches from a large image dataset. These candidate patches are then integrated into the visual description, presenting the final reconstructed images. Our preliminary but promising results demonstrate the effectiveness of our proposed image coding scheme towards perceptual quality. Our proposed image compression scheme provides a feasible approach to make use of the visual correlation among images.

Model merging is widely used in many domains such as geography modeling, 3D design of highway and railway route and so on, so it is essential to design an efficient model merging algorithm. The objective of this paper is to research on models merging algorithm based on Delaunay triangulation. A method of obtaining model merged intersection lines quickly and an algorithm of searching triangles on the inside of the intersection lines fast are proposed. For the purpose of implementing Delaunay triangulation models mergence, two main steps, such as insert the merged intersection lines into the Initial Delaunay Triangulation ( IDT) , and cut the triangles inside of the intersection lines in IDT out, are adopted. The processes of models merging are expatiated detailedly. The proposed algorithms are implemented by using VC+ + programming language and tested by some experiment data. The experimental result validated the correctness and feasibility about the proposed algorithms.

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.