A Study of image compression based transmission algorithm Using SPIHT for low bit rate application

Image compression is internationally recognized up to the minute tools for decrease the communication bandwidth and save the transmitting power. It should reproduce a good quality image after compression at low bit rates. Set partitioning in hierarchical trees (SPIHT) is wavelet based computationally very fast and among the best image compression based transmission algorithm that offers good compression ratios, fast execution time and good image quality. Precise Rate Control (PRC) is the distinct characteristic of SPIHT. Image compression-based on Precise Rate Control and fast coding time are principally analyzed in this paper. Experimental result shows that, in the case of low bit-rate, the modified algorithm with fast Coding Time and Precise Rate Control can reduce the execution time and improves the quality of reconstructed image in both PSNR and perceptual when compare to at the same low bit rate

Joint Source-Channel Coding for Image Transmission over Underlay Multichannel Cognitive Radio Networks

The increasing prominence of wireless applications exacerbates the problem of radio spectrum scarcity and promotes the usage of Cognitive Radio (CR) in wireless networks. With underlay dynamic spectrum access, CRs can operate alongside Primary Users, the incumbent of a spectrum band, as long as they limit the interference to the Primary Users below a certain threshold. Multimedia streaming transmissions face stringent Quality of Services constraints on top of the CR interference constraints, as some packets in the data stream have higher levels of importance and are the most vulnerable to packet loss over the channel. This raises a need for Unequal Error Protection (ULP) for multimedia streams transmissions, in which the channel encoder assigns different amount of error correction to different parts of the data stream, thereby protecting more the most valuable parts of the stream from packet loss problems. This research presents an end-to-end system setup for image transmission, utilizing ULP as part of a Joint Source-Channel Coding scheme over a multichannel CR network operating through underlay dynamic spectrum access. The setup features a Set Partitioning in Hierarchical Trees (SPIHT) source encoder, and Reed-Solomon forward error correction channel coding, and uses their properties to devise an ULP framework that maximizes the quality of the received image

A Lossy Colour Image Compression Using Integer Wavelet Transforms and Binary Plane Transform

In the recent period, image data compression is the major component of communication and storage systems where the uncompressed images requires considerable compression technique, which should be capable to reduce the crippling disadvantages of data transmission and image storage. In the research paper, the novel image compression technique is proposed which is based on the spatial domain and quite effective for the compression of images. However, the performance of the proposed methodology is compared with the conventional compression techniques (Joint Photographic Experts Group) JPEG and set partitioning in hierarchical trees (SPIHT) using the evaluation metrics compression ratio and peak signal to noise ratio. It is evaluated that Integer wavelets with binary plane technique is more effective compression technique than JPEG and SPIHT as it provided more efficient quality metrics values and visual quality

Compression Efficiency for Combining Different Embedded Image Compression Techniques with Huffman Encoding

This thesis presents a technique for image compression which uses the different embedded Wavelet based image coding in combination with Huffman- encoder(for further compression). There are different types of algorithms available for lossy image compression out of which Embedded Zerotree Wavelet(EZW), Set Partitioning in Hierarchical Trees (SPIHT) and Modified SPIHT algorithms are the some of the important compression techniques. EZW algorithm is based on progressive encoding to compress an image into a bit stream with increasing accuracy. The EZW encoder was originally designed to operate on 2D images, but it can also use to other dimensional signals. Progressive encoding is also called as embedded encoding. Main feature of ezw algorithm is capability of meeting an exact target bit rate with corresponding rate distortion rate(RDF). Set Partitioning in Hierarchical Trees (SPIHT) is an improved version of EZW and has become the general standard of EZW. SPIHT is a very efficient image compression algorithm that is based on the idea of coding groups of wavelet coefficients as zero trees. Since the order in which the subsets are tested for significance is important in a practical implementation the significance information is stored in three ordered lists called list of insignificant sets (LIS) list of insignificant pixels (LIP) and list of significant pixels (LSP). Modified SPIHT algorithm and the preprocessing techniques provide significant quality (both subjectively and objectively) reconstruction at the decoder with little additional computational complexity as compared to the previous techniques. This proposed method can reduce redundancy to a certain extend. Simulation results show that these hybrid algorithms yield quite promising PSNR values at low bitrates

State of the art in 2D content representation and compression

Livrable D1.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D3.1 du projet

Hierarchical quantization indexing for wavelet and wavelet packet image coding

In this paper, we introduce the quantization index hierarchy, which is used for efficient coding of quantized wavelet and wavelet packet coefficients. A hierarchical classification map is defined in each wavelet subband, which describes the quantized data through a series of index classes. Going from bottom to the top of the tree, neighboring coefficients are combined to form classes that represent some statistics of the quantization indices of these coefficients. Higher levels of the tree are constructed iteratively by repeating this class assignment to partition the coefficients into larger Subsets. The class assignments are optimized using a rate-distortion cost analysis. The optimized tree is coded hierarchically from top to bottom by coding the class membership information at each level of the tree. Context-adaptive arithmetic coding is used to improve coding efficiency. The developed algorithm produces PSNR results that are better than the state-of-art wavelet-based and wavelet packet-based coders in literature.This research was supported by Isik University BAP-05B302 GrantPublisher's Versio

Data hiding in multimedia - theory and applications

Multimedia data hiding or steganography is a means of communication using subliminal channels. The resource for the subliminal communication scheme is the distortion of the original content that can be tolerated. This thesis addresses two main issues of steganographic communication schemes: 1. How does one maximize the distortion introduced without affecting fidelity of the content? 2. How does one efficiently utilize the resource (the distortion introduced) for communicating as many bits of information as possible? In other words, what is a good signaling strategy for the subliminal communication scheme? Close to optimal solutions for both issues are analyzed. Many techniques for the issue for maximizing the resource, viz, the distortion introduced imperceptibly in images and video frames, are proposed. Different signaling strategies for steganographic communication are explored, and a novel signaling technique employing a floating signal constellation is proposed. Algorithms for optimal choices of the parameters of the signaling technique are presented. Other application specific issues like the type of robustness needed are taken into consideration along with the established theoretical background to design optimal data hiding schemes. In particular, two very important applications of data hiding are addressed - data hiding for multimedia content delivery, and data hiding for watermarking (for proving ownership). A robust watermarking protocol for unambiguous resolution of ownership is proposed

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201