Development of Novel Image Compression Algorithms for Portable Multimedia Applications

Portable multimedia devices such as digital camera, mobile d evices, personal digtal assistants (PDAs), etc. have limited memory, battery life and processing power. Real time processing and transmission using these devices requires image compression algorithms that can compress efficiently with reduced complexity. Due to limited resources, it is not always possible to implement the best algorithms inside these devices. In uncompressed form, both raw and image data occupy an unreasonably large space. However, both raw and image data have a significant amount of statistical and visual redundancy. Consequently, the used storage space can be efficiently reduced by compression. In this thesis, some novel low complexity and embedded image compression algorithms are developed especially suitable for low bit rate image compression using these devices. Despite the rapid progress in the Internet and multimedia technology, demand for data storage and data transmission bandwidth continues to outstrip the capabil- ities of available technology. The browsing of images over In ternet from the image data sets using these devices requires fast encoding and decodin g speed with better rate-distortion performance. With progressive picture build up of the wavelet based coded images, the recent multimedia applications demand goo d quality images at the earlier stages of transmission. This is particularly important if the image is browsed over wireless lines where limited channel capacity, storage and computation are the deciding parameters. Unfortunately, the performance of JPEG codec degrades at low bit rates because of underlying block based DCT transforms. Altho ugh wavelet based codecs provide substantial improvements in progressive picture quality at lower bit rates, these coders do not fully exploit the coding performance at lower bit rates. It is evident from the statistics of transformed images that the number of significant coefficients having magnitude higher than earlier thresholds are very few. These wavelet based codecs code zero to each insignificant subband as it moves from coarsest to finest subbands. It is also demonstrated that there could be six to sev en bit plane passes where wavelet coders encode many zeros as many subbands are likely to be insignificant with respect to early thresholds. Bits indicating insignificance of a coefficient or subband are required, but they don’t code information that reduces distortion of the reconstructed image. This leads to reduction of zero distortion for an increase in non zero bit-rate. Another problem associated with wavelet based coders such as Set partitioning in hierarchical trees (SPIHT), Set partitioning embedded block (SPECK), Wavelet block-tree coding (WBTC) is because of the use of auxiliary lists. The size of list data structures increase exponentially as more and more eleme nts are added, removed or moved in each bitplane pass. This increases the dynamic memory requirement of the codec, which is a less efficient feature for hardware implementations. Later, many listless variants of SPIHT and SPECK, e.g. No list SPIHT (NLS) and Listless SPECK (LSK) respectively are developed. However, these algorithms have similar rate distortion performances, like the list based coders. An improved LSK (ILSK)algorithm proposed in this dissertation that improves the low b it rate performance of LSK by encoding much lesser number of symbols (i.e. zeros) to several insignificant subbands. Further, the ILSK is combined with a block based transform known as discrete Tchebichef transform (DTT). The proposed new coder isnamed as Hierar-chical listless DTT (HLDTT). DTT is chosen over DCT because of it’s similar energy compaction property like discrete cosine transform (DCT). It is demonstrated that the decoded image quality using HLDTT has better visual performance (i.e., Mean Structural Similarity) than the images decoded using DCT based embedded coders in most of the bit rates. The ILSK algorithm is also combined with Lift based wavelet tra nsform to show the superiority over JPEG2000 at lower rates in terms of peak signal-to-noise ratio (PSNR). A full-scalable and random access decodable listless algorithm is also developed which is based on lift based ILSK. The proposed algorithm named as scalable listless embedded block partitioning (S-LEBP) generates bit stream that offer increasing signal-to-noise ratio and spatial resolution. These are very useful features for transmission of images in a heterogeneous network that optimally service each user according to available bandwidth and computing needs. Random access decoding is a very useful feature for extracting/manipulating certain ar ea of an image with minimal decoding work. The idea used in ILSK is also extended to encode and decode color images. The proposed algorithm for coding color images is named as Color listless embedded block partitioning (CLEBP) algorithm. The coding efficiency of CLEBP is compared with Color SPIHT (CSPIHT) and color variant of WBTC algorithm. From the simulation results, it is shown that CLEBP exhibits a significant PSNR performance improvement over the later two algorithms on various types of images. Although many modifications to NLS and LSK have been made, the listless modification to WBTC algorithm has not been reported in the literature. Therefore,a listless variant of WBTC (named as LBTC) algorithm is proposed. LBTC not only reduces the memory requirement by 88-89% but also increases the encoding and decoding speed, while preserving the rate-distortion perform ance at the same time. Further, the combination of DCT with LBTC (named as DCT LBT) and DTT with LBTC (named as Hierarchical listless DTT, HLBTDTT) are compared with some state-of-the-art DCT based embedded coders. It is also shown that the proposed DCT-LBT and HLBT-DTT show significant PSNR improvements over almost all the embedded coders in most of the bit rates. In some multimedia applications e.g., digital camera, camco rders etc., the images always need to have a fixed pre-determined high quality. The extra effort required for quality scalability is wasted. Therefore, non-embedded algo rithms are best suited for these applications. The proposed algorithms can be made non-embedded by encoding a fixed set of bit planes at a time. Instead, a sparse orthogonal transform matrix is proposed, which can be integrated in a JEPG baseline coder. The proposed matrix promises a substantial reduction in hardware complexity with amarginal loss of image quality on a considerable range of bit rates than block based DCT or Integer DCT

Dimensionality reduction and sparse representations in computer vision

The proliferation of camera equipped devices, such as netbooks, smartphones and game stations, has led to a significant increase in the production of visual content. This visual information could be used for understanding the environment and offering a natural interface between the users and their surroundings. However, the massive amounts of data and the high computational cost associated with them, encumbers the transfer of sophisticated vision algorithms to real life systems, especially ones that exhibit resource limitations such as restrictions in available memory, processing power and bandwidth. One approach for tackling these issues is to generate compact and descriptive representations of image data by exploiting inherent redundancies. We propose the investigation of dimensionality reduction and sparse representations in order to accomplish this task. In dimensionality reduction, the aim is to reduce the dimensions of the space where image data reside in order to allow resource constrained systems to handle them and, ideally, provide a more insightful description. This goal is achieved by exploiting the inherent redundancies that many classes of images, such as faces under different illumination conditions and objects from different viewpoints, exhibit. We explore the description of natural images by low dimensional non-linear models called image manifolds and investigate the performance of computer vision tasks such as recognition and classification using these low dimensional models. In addition to dimensionality reduction, we study a novel approach in representing images as a sparse linear combination of dictionary examples. We investigate how sparse image representations can be used for a variety of tasks including low level image modeling and higher level semantic information extraction. Using tools from dimensionality reduction and sparse representation, we propose the application of these methods in three hierarchical image layers, namely low-level features, mid-level structures and high-level attributes. Low level features are image descriptors that can be extracted directly from the raw image pixels and include pixel intensities, histograms, and gradients. In the first part of this work, we explore how various techniques in dimensionality reduction, ranging from traditional image compression to the recently proposed Random Projections method, affect the performance of computer vision algorithms such as face detection and face recognition. In addition, we discuss a method that is able to increase the spatial resolution of a single image, without using any training examples, according to the sparse representations framework. In the second part, we explore mid-level structures, including image manifolds and sparse models, produced by abstracting information from low-level features and offer compact modeling of high dimensional data. We propose novel techniques for generating more descriptive image representations and investigate their application in face recognition and object tracking. In the third part of this work, we propose the investigation of a novel framework for representing the semantic contents of images. This framework employs high level semantic attributes that aim to bridge the gap between the visual information of an image and its textual description by utilizing low level features and mid level structures. This innovative paradigm offers revolutionary possibilities including recognizing the category of an object from purely textual information without providing any explicit visual example

SPARSE RECOVERY BY NONCONVEX LIPSHITZIAN MAPPINGS

In recent years, the sparsity concept has attracted considerable attention in areas of applied mathematics and computer science, especially in signal and image processing fields. The general framework of sparse representation is now a mature concept with solid basis in relevant mathematical fields, such as probability, geometry of Banach spaces, harmonic analysis, theory of computability, and information-based complexity. Together with theoretical and practical advancements, also several numeric methods and algorithmic techniques have been developed in order to capture the complexity and the wide scope that the theory suggests. Sparse recovery relays over the fact that many signals can be represented in a sparse way, using only few nonzero coefficients in a suitable basis or overcomplete dictionary. Unfortunately, this problem, also called `0-norm minimization, is not only NP-hard, but also hard to approximate within an exponential factor of the optimal solution. Nevertheless, many heuristics for the problem has been obtained and proposed for many applications. This thesis provides new regularization methods for the sparse representation problem with application to face recognition and ECG signal compression. The proposed methods are based on fixed-point iteration scheme which combines nonconvex Lipschitzian-type mappings with canonical orthogonal projectors. The first are aimed at uniformly enhancing the sparseness level by shrinking effects, the latter to project back into the feasible space of solutions. In the second part of this thesis we study two applications in which sparseness has been successfully applied in recent areas of the signal and image processing: the face recognition problem and the ECG signal compression problem

Efficient and Robust Video Steganography Algorithms for Secure Data Communication

Over the last two decades, the science of secretly embedding and communicating data has gained tremendous significance due to the technological advancement in communication and digital content. Steganography is the art of concealing secret data in a particular interactive media transporter such as text, audio, image, and video data in order to build a covert communication between authorized parties. Nowadays, video steganography techniques are important in many video-sharing and social networking applications such as Livestreaming, YouTube, Twitter, and Facebook because of noteworthy developments in advanced video over the Internet. The performance of any steganography method, ultimately, relies on the imperceptibility, hiding capacity, and robustness against attacks. Although many video steganography methods exist, several of them lack the preprocessing stages. In addition, less security, low embedding capacity, less imperceptibility, and less robustness against attacks are other issues that affect these algorithms. This dissertation investigates and analyzes cutting edge video steganography techniques in both compressed and raw domains. Moreover, it provides solutions for the aforementioned problems by proposing new and effective methods for digital video steganography. The key objectives of this research are to develop: 1) a highly secure video steganography algorithm based on error correcting codes (ECC); 2) an increased payload video steganography algorithm in the discrete wavelet domain based on ECC; 3) a novel video steganography algorithm based on Kanade-Lucas-Tomasi (KLT) tracking and ECC; 4) a robust video steganography algorithm in the wavelet domain based on KLT tracking and ECC; 5) a new video steganography algorithm based on the multiple object tracking (MOT) and ECC; and 6) a robust and secure video steganography algorithm in the discrete wavelet and discrete cosine transformations based on MOT and ECC. The experimental results from our research demonstrate that our proposed algorithms achieve higher embedding capacity as well as better imperceptibility of stego videos. Furthermore, the preprocessing stages increase the security and robustness of the proposed algorithms against attacks when compared to state-of-the-art steganographic methods

Colour image coding with wavelets and matching pursuit

This thesis considers sparse approximation of still images as the basis of a lossy compression system. The Matching Pursuit (MP) algorithm is presented as a method particularly suited for application in lossy scalable image coding. Its multichannel extension, capable of exploiting inter-channel correlations, is found to be an efficient way to represent colour data in RGB colour space. Known problems with MP, high computational complexity of encoding and dictionary design, are tackled by finding an appropriate partitioning of an image. The idea of performing MP in the spatio-frequency domain after transform such as Discrete Wavelet Transform (DWT) is explored. The main challenge, though, is to encode the image representation obtained after MP into a bit-stream. Novel approaches for encoding the atomic decomposition of a signal and colour amplitudes quantisation are proposed and evaluated. The image codec that has been built is capable of competing with scalable coders such as JPEG 2000 and SPIHT in terms of compression ratio

The Noise Reduction over Wireless Channel Using Vector Quantization Compression and Filtering

The transmission of compressed data over wireless channel conditions represents a big challenge. The idea of providing robust transmission gets a lot of attention in field of research. In this paper we study the effect of the noise over wireless channel. We use the model of Gilbert-Elliot to represent the channel. The parameters of the model are selected to represent three cases of channel. As data for transmission we use images in gray level size 512x512. To minimize bandwidth usage we compressed the image with vector quantization also in this compression technique we study the effect of the codebook in the robustness of transmission so we use different algorithms to generate the codebook for the vector quantization finally we study the restoration efficiency of received image using filtering and indices recovery technique

Biometric Applications Based on Multiresolution Analysis Tools

This dissertation is dedicated to the development of new algorithms for biometric applications based on multiresolution analysis tools. Biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual\u27s identity. Biometrics can measure physiological, behavioral, physical and chemical characteristics of an individual. Physiological characteristics are based on measurements derived from direct measurement of a part of human body, such as, face, fingerprint, iris, retina etc. We focussed our investigations to fingerprint and face recognition since these two biometric modalities are used in conjunction to obtain reliable identification by various border security and law enforcement agencies. We developed an efficient and robust human face recognition algorithm for potential law enforcement applications. A generic fingerprint compression algorithm based on state of the art multiresolution analysis tool to speed up data archiving and recognition was also proposed. Finally, we put forth a new fingerprint matching algorithm by generating an efficient set of fingerprint features to minimize false matches and improve identification accuracy. Face recognition algorithms were proposed based on curvelet transform using kernel based principal component analysis and bidirectional two-dimensional principal component analysis and numerous experiments were performed using popular human face databases. Significant improvements in recognition accuracy were achieved and the proposed methods drastically outperformed conventional face recognition systems that employed linear one-dimensional principal component analysis. Compression schemes based on wave atoms decomposition were proposed and major improvements in peak signal to noise ratio were obtained in comparison to Federal Bureau of Investigation\u27s wavelet scalar quantization scheme. Improved performance was more pronounced and distinct at higher compression ratios. Finally, a fingerprint matching algorithm based on wave atoms decomposition, bidirectional two dimensional principal component analysis and extreme learning machine was proposed and noteworthy improvements in accuracy were realized

De-Duplication of Person's Identity Using Multi-Modal Biometrics

The objective of this work is to explore approaches to create unique identities by the de-duplication process using multi-modal biometrics. Various government sectors in the world provide different services and welfare schemes for the beneffit of the people in the society using an identity number. A unique identity (UID) number assigned for every person would obviate the need for a person to produce multiple documentary proofs of his/her identity for availing any government/private services. In the process of creating unique identity of a person, there is a possibility of duplicate identities as the same person might want to get multiple identities in order to get extra beneffits from the Government. These duplicate identities can be eliminated by the de-duplication process using multi-modal biometrics, namely, iris, ngerprint, face and signature. De-duplication is the process of removing instances of multiple enrollments of the same person using the person's biometric data. As the number of people enrolledinto the biometric system runs into billions, the time complexity increases in the de duplication process. In this thesis, three different case studies are presented to address the performance issues of de-duplication process in order to create unique identity of a person