Thesis (MScEng)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: Data volumes produced by the next generation of earth observation sensors have increased greatly in recent years. Sensors are generating more data than can be easily stored onboard satellites and transmitted to the ground-stations. There are two strategies for solving this problem. The first is to process all images onboard the satellite, and only extract the useful or valuable information. The second is to use a compression algorithm to reduce the data volume. This thesis looks at both strategies and then focusses on an evaluation of the Embedded Zerotree Wavelet (EZW) algorithm, a wavelet-based lossy image compression algorithm, as a solution to reduce the data volumes. Possible hardware implementation strategies for this algorithm are also explored. Finally, a suggested implementation of the EZW algorithm is compared with the FlexWave-II system and with JPEG2000. / AFRIKAANSE OPSOMMING: Die data volumes wat deur die nuwe generasie van aardobservasiesensore geproduseer word, het dramaties vergroot in die laaste paar jaar. Daar word nou meer data geproduseer as wat aanboord van die satelliet gestoor kan word en meer as wat in die beperkte kommunikasietyd aan die grondstasie gestuur kan word. Daar is twee strategiee om hierdie probleem aan te spreek. Eerstens kan beelde aanboord die satelliet verwerk word om die belangrikste of waardevolste inligting uit te haal en die res van die data word dan geskrap. Die alternatief is om 'n beeldkompressie-algoritme te gebruik om die data te verminder. Hierdie tesis ondersoek hierdie strategieë en fokus dan op 'n evaluasie van die "Embedded Zerotree Wavelet" -algoritme. Die EZW-algoritme is 'n verlieserige, golfie-gebaseerde beeldkompressie-algoritme. Moontlike hardeware-implementeringsopsies word ondersoek en die resultate van een voorgestelde opsie word vergelyk met die FlexWave-II stelsel asook die nuwe JPEG2000-standaard.

With the evolution of multimedia systems, image and video compression is becoming the key enabling technology for delivering various image/video services over heterogeneous networks. The basic goal of image data compression is to reduce the bit rate for transmission and storage while either maintaining the original quality of the data or providing an acceptable quality. This thesis proposes a new wavelet transform for lossless compression of images with application to medical images. The transform uses integer arithmetic and is very computationally efficient. Then a new color image transformation, which is reversible and uses integer arithmetic, is proposed. The transformation reduces the redundancy among the red, green, and blue color bands. It approximates the luminance and chrominance components of the YIQ coordinate system. This transformation involves no floating point/integer multiplications or divisions, and is, therefore, very suitable for real-time applications where the number of CPU cycles needs to be kept to a minimum. A technique for lossy compression of an image data base is also proposed. The technique uses a wavelet transform and vector quantization for compression. The discrete cosine transform is applied to the coarsest scale wavelet coefficients to achieve even higher compression ratios without any significant increase in computational complexity. Wavelet denoising is used to reduce the image artifacts generated by quantizing the discrete cosine transform coefficients. This improves the subjective quality of the decompressed images for very low bit rate images (less than 0.5 bits per pixel). The thesis also deals with the real-time implementation of the wavelet transform. The new wavelet transform has been applied to speech signals. Both lossless and lossy techniques for speech coding have been implemented. The lossless technique involves using the reversible integer-arithmetic wavelet transform and Huffman coding to obtain the compressed bitstream. The lossy technique, on the other hand, quantizes the wavelet coefficients to obtain higher compression ratio at the expense of some degradation in sound quality. The issues related to real-time wavelet compression are also discussed. Due to the limited size of memory on a DSP, a wavelet transform had to be applied to an input signal of finite length. The effects of varying the signal length on compression performance are also studied for different reversible wavelet transforms. The limitations of the proposed techniques are discussed and recommendations for future research are provided. / Graduate

The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable for high performance imaging in the IoT, such as Intelligent Traffic Surveillance (ITS) and Telemedicine. Due to power consumption, which has become a major concern in any portable application, a low-power design of SBPG is proposed to achieve an energy- efficient SBPG design. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. Higher value of PSNR also shows how robust the algorithm is to different types of attack. From the results obtained for the energy- efficient SBPG design, it can be observed that the power consumption is substantially reduced, up to 19%.

The scope of the present dissertation is a deep lossy compression of still and moving grayscale pictures while maintaining their fidelity, with a specific goal of creating a working prototype of a software system for use in low bandwidth transmission of still satellite imagery and weather briefings with the best preservation of features considered important by the end user.

Nowadays, still images are used everywhere in the digital world. The shortages of storage capacity and transmission bandwidth make efficient compression solutions essential. A revolutionary mathematics tool, wavelet transform, has already shown its power in image processing. Minimage, the major topic of this paper, is an application that compresses still images by wavelets. Minimage is used to compress grayscale images and true color images. It implements the wavelet transform to code standard BMP image files to LET wavelet image files, which is defined in Minimage. The code is written in C++ on the Microsoft Windows NT platform. This paper illustrates the design and implementation details in MinImage according to the image compression stages. First, the preprocessor generates the wavelet transform blocks. Second, the basic wavelet decomposition is applied to transform the image data to the wavelet coefficients. The discrete wavelet transforms are the kernel component of MinImage and are discussed in detail. The different wavelet transforms can be plugged in to extend the functionality of MinImage. The third step is the quantization. The standard scalar quantization algorithm and the optimized quantization algorithm, as well as the dequantization, are described. The last part of MinImage is the entropy-coding schema. The reordering of the coefficients based on the Peano Curve and the different entropy coding methods are discussed. This paper also gives the specification of the wavelet compression parameters adjusted by the end user. The interface, parameter specification, and analysis of MinImage are shown in the final appendix.

Recent research in transform-based image compression has focused on the wavelet transform due to its superior performance over other transforms. Performance is often measured solely in terms of peak signal-to-noise ratio (PSNR) and compression algorithms are optimized for this quantitative metric. The performance in terms of subjective quality is typically not evaluated. Moreover, the sensitivities of the human visual system (HVS) are often not incorporated into compression schemes. This paper develops new wavelet models of the HVS and illustrates their performance for various scalar wavelet and multiwavelet transforms. The performance is measured quantitatively (PSNR) and qualitatively using our new perceptual testing procedure. Our new HVS model is comprised of two components: CSF masking and asymmetric compression. CSF masking weights the wavelet coefficients according to the contrast sensitivity function (CSF)---a model of humans' sensitivity to spatial frequency. This mask gives the most perceptible information the highest priority in the quantizer. The second component of our HVS model is called asymmetric compression. It is well known that humans are more sensitive to luminance stimuli than they are to chrominance stimuli; asymmetric compression quantizes the chrominance spaces more severely than the luminance component. The results of extensive trials indicate that our HVS model improves both quantitative and qualitative performance. These trials included 14 observers, 4 grayscale images and 10 color images (both natural and synthetic). For grayscale images, although our HVS scheme lowers PSNR, it improves subjective quality. For color images, our HVS model improves both PSNR and subjective quality. A benchmark for our HVS method is the latest version of the international image compression standard---JPEG2000. In terms of subjective quality, our scheme is superior to JPEG2000 for all images; it also outperforms JPEG2000 by 1 to 3 dB in PSNR. / Master of Science

This research focuses on the implementation of the efficient image compression system among the many potential applications of a transform imager system. The study includes implementing the image compression system using a transform imager, developing a novel image compression algorithm for the system, and improving the performance of the image compression system through efficient encoding and decoding algorithms for vector quantization. A transform imaging system is implemented using a transform imager, and the baseline JPEG compression algorithm is implemented and tested to verify the functionality and performance of the transform imager system. The computational reduction in digital processing is investigated from two perspectives, algorithmic and implementation. Algorithmically, a novel wavelet-based embedded image compression algorithm using dynamic index reordering vector quantization (DIRVQ) is proposed for the system. DIRVQ makes it possible for the proposed algorithm to achieve superior performance over the embedded zero-tree wavelet (EZW) algorithm and the successive approximation vector quantization (SAVQ) algorithm. However, because DIRVQ requires intensive computational complexity, additional focus is placed on the efficient implementation of DIRVQ, and highly efficient implementation is achieved without a compromise in performance.

When geographical distance poses as a barrier, telepathology is designed to offer pathologists the opportunity to replicate their normal activities by using an alternative means of practice. The rapid progression in technology has greatly influenced the appeal of telepathology and its use in multiple domains. To that point, telepathology systems help to afford teleconsultation services for remote locations, improve the workload distribution in clinical environments, measure quality assurance, and also enhance educational programs. While telepathology is an attractive method to many potential users, the resource requirements for digitizing microscopic specimens have hindered widespread adoption. The use of image compression is extremely critical to help advance the pervasiveness of digital images in pathology. For this research, we characterize two different methods that we use to assess compression of pathology images. Our first method is characterized by the fact that image quality is human-based and completely subjective in terms of interpretation. Our second method is characterized by the fact that image analysis is introduced by using machine-based interpretation to provide objective results. Additionally, the objective outcomes from the image analysis may also be used to help confirm tumor classification. With these two methods in mind, the purpose of this dissertation is to quantify the effects of image compression on data interpretation as seen by human experts and a computerized algorithm for use in telepathology.

No description available.

Thesis (MSc)--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: The nonlinear LULU smoothers excel at removing impulsive noise from sequences and possess a variety of theoretical properties that make it possible to perform a so-called Discrete Pulse Transform, which is a novel multiresolution analysis technique that decomposes a sequence into resolution levels with a large amount of structure, analogous to a Discrete Wavelet Transform. We explore the use of a one-dimensional Discrete Pulse Transform as the central element in a digital image compressor. We depend crucially on the ability of space-filling scanning orders to map the two-dimensional image data to one dimension, sacrificing as little image structure as possible. Both lossless and lossy image compression are considered, leading to five new image compression schemes that give promising results when compared to state-of-the-art image compressors. / AFRIKAANSE OPSOMMING: Die nielineêre LULU gladstrykers verwyder impulsiewe geraas baie goed uit rye en besit verskeie teoretiese eienskappe wat dit moontlik maak om ’n sogenoemde Diskrete Puls Transform uit te voer; ’n nuwe multiresolusie analise tegniek wat ’n ry opbreek in ’n versameling resolusie vlakke wat ’n groot hoeveelheid struktuur bevat, soortgelyk tot ’n Diskrete Golfie Transform. Ons ondersoek of ’n eendimensionele Diskrete Puls Transform as die sentrale element in ’n digitale beeld kompressor gebruik kan word. Ons is afhanklik van ruimtevullende skandeer ordes om die tweedimensionele beelddata om te skakel na een dimensie, sonder om te veel beeld struktuur te verloor. Vyf nuwe beeld kompressie skemas word bespreek. Hierdie skemas lewer belowende resultate wanneer dit met die beste hedendaagse beeld kompressors vergelyk word.