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

Image Compression Techniques: A Survey in Lossless and Lossy algorithms

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression

DCT-Based Image Feature Extraction and Its Application in Image Self-Recovery and Image Watermarking

Feature extraction is a critical element in the design of image self-recovery and watermarking algorithms and its quality can have a big influence on the performance of these processes. The objective of the work presented in this thesis is to develop an effective methodology for feature extraction in the discrete cosine transform (DCT) domain and apply it in the design of adaptive image self-recovery and image watermarking algorithms. The methodology is to use the most significant DCT coefficients that can be at any frequency range to detect and to classify gray level patterns. In this way, gray level variations with a wider range of spatial frequencies can be looked into without increasing computational complexity and the methodology is able to distinguish gray level patterns rather than the orientations of simple edges only as in many existing DCT-based methods. The proposed image self-recovery algorithm uses the developed feature extraction methodology to detect and classify blocks that contain significant gray level variations. According to the profile of each block, the critical frequency components representing the specific gray level pattern of the block are chosen for encoding. The code lengths are made variable depending on the importance of these components in defining the block’s features, which makes the encoding of critical frequency components more precise, while keeping the total length of the reference code short. The proposed image self-recovery algorithm has resulted in remarkably shorter reference codes that are only 1/5 to 3/5 of those produced by existing methods, and consequently a superior visual quality in the embedded images. As the shorter codes contain the critical image information, the proposed algorithm has also achieved above average reconstruction quality for various tampering rates. The proposed image watermarking algorithm is computationally simple and designed for the blind extraction of the watermark. The principle of the algorithm is to embed the watermark in the locations where image data alterations are the least visible. To this end, the properties of the HVS are used to identify the gray level image features of such locations. The characteristics of the frequency components representing these features are identifying by applying the DCT-based feature extraction methodology developed in this thesis. The strength with which the watermark is embedded is made adaptive to the local gray level characteristics. Simulation results have shown that the proposed watermarking algorithm results in significantly higher visual quality in the watermarked images than that of the reported methods with a difference in PSNR of about 2.7 dB, while the embedded watermark is highly robustness against JPEG compression even at low quality factors and to some other common image processes. The good performance of the proposed image self-recovery and watermarking algorithms is an indication of the effectiveness of the developed feature extraction methodology. This methodology can be applied in a wide range of applications and it is suitable for any process where the DCT data is available