Stack-run adaptive wavelet image compression

We report on the development of an adaptive wavelet image coder based on stack-run representation of the quantized coefficients. The coder works by selecting an optimal wavelet packet basis for the given image and encoding the quantization indices for significant coefficients and zero runs between coefficients using a 4-ary arithmetic coder. Due to the fact that our coder exploits the redundancies present within individual subbands, its addressing complexity is much lower than that of the wavelet zerotree coding algorithms. Experimental results show coding gains of up to 1:4dB over the benchmark wavelet coding algorithm

Evaluation of transform based image coders, using different transforms and techniques in the transform domain

This paper addresses the most relevant aspects of lossy image coding techniques, and presents an evaluation study on this subject, using several transforms and different methods in the transform domain. We developed different transform based image coders/decoders (codecs) using different transforms, such as the discrete cosine transform, the discrete wavelet transform and the S transform. Besides JPEG Baseline, we also use other techniques and methods in the transform domain such as a DWT based JPEG-like (JPEG DWT), a JPEG DWT with visual threshold (JPEG-VT), a JPEG–like coder based on the ST, and an EZW coder. The codecs were programmed in MATLAB™, using custom and built-in functions. The structures of the codecs are presented, also as some experimental results which allow us evaluate them, and support this study

Overview of Image Processing and Various Compression Schemes

Image processing is key research among researchers. Compression of images are required when need of transmission or storage of images. Demand of multimedia growth, contributes to insufficient bandwidth of network and memory storage device. Advance imaging requires capacity of extensive amounts of digitized information. Therefore data compression is more required for reducing data redundancy to save more hardware space and transmission bandwidth. Various techniques are given for image compression. Some of which are discussed in this paper

Perceptual lossless medical image coding

A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts has shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder

Perceptual Zero-Tree Coding with Efficient Optimization for Embedded Platforms

This study proposes a block-edge-based perceptual zero-tree coding (PZTC) method, which is implemented with efficientoptimization on the embedded platform. PZTC combines two novel compression concepts for coding efficiency and quality:block-edge detection (BED) and the low-complexity and low-memory entropy coder (LLEC). The proposed PZTC wasimplemented as a fixed-point version and optimized on the DSP-based platform based on both the presented platformindependentand platform-dependent optimization technologies. For platform-dependent optimization, this study examinesthe fixed-point PZTC and analyzes the complexity to optimize PZTC toward achieving an optimal coding efficiency.Furthermore, hardware-based platform-dependent optimizations are presented to reduce the memory size. Theperformance, such as compression quality and efficiency, is validated by experimental results

RGB Medical Video Compression Using Geometric Wavelet

The video compression is used in a wide of applications from medical domain especially in telemedicine. Compared to the classical transforms, wavelet transform has significantly better performance in horizontal, vertical and diagonal directions. Therefore, this transform introduces high discontinuities in complex geometrics. However, to detect complex geometrics is one key challenge for the high efficient compression. In order to capture anisotropic regularity along various curves a new efficient and precise transform termed by bandelet basis, based on DWT, quadtree decomposition and optical flow is proposed in this paper. To encode significant coefficients we use efficient coder SPIHT. The experimental results show that the proposed algorithm DBT-SPIHT for low bit rate (0.3Mbps) is able to reduce up to 37.19% and 28.20% of the complex geometrics detection compared to the DWT-SPIHT and DCuT-SPIHT algorithm

A Novel Image Compression Method Based on Classified Energy and Pattern Building Blocks

In this paper, a novel image compression method based on generation of the so-called classified energy and pattern blocks (CEPB) is introduced and evaluation results are presented. The CEPB is constructed using the training images and then located at both the transmitter and receiver sides of the communication system. Then the energy and pattern blocks of input images to be reconstructed are determined by the same way in the construction of the CEPB. This process is also associated with a matching procedure to determine the index numbers of the classified energy and pattern blocks in the CEPB which best represents (matches) the energy and pattern blocks of the input images. Encoding parameters are block scaling coefficient and index numbers of energy and pattern blocks determined for each block of the input images. These parameters are sent from the transmitter part to the receiver part and the classified energy and pattern blocks associated with the index numbers are pulled from the CEPB. Then the input image is reconstructed block by block in the receiver part using a mathematical model that is proposed. Evaluation results show that the method provides considerable image compression ratios and image quality even at low bit rates.The work described in this paper was funded by the Isik University Scientific Research Fund (Project contract no. 10B301). The author would like to thank to Professor B. S. Yarman (Istanbul University, College of Engineering, Department of Electrical-Electronics Engineering), Assistant Professor Hakan Gurkan (Isik University, Engineering Faculty, Department of Electrical-Electronics Engineering), the researchers in the International Computer Science Institute (ICSI), Speech Group, University of California at Berkeley, CA, USA and the researchers in the SRI International, Speech Technology and Research (STAR) Laboratory, Menlo Park, CA, USA for many helpful discussions on this work during his postdoctoral fellow years. The author also would like to thank the anonymous reviewers for their valuable comments and suggestions which substantially improved the quality of this paperPublisher's Versio