Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 107-117).

Image analysis and graphics synthesis can be achieved with learning techniques using directly image examples without physically-based, 3D models. In our technique: -- the mapping from novel images to a vector of "pose" and "expression" parameters can be learned from a small set of example images using a function approximation technique that we call an analysis network; -- the inverse mapping from input "pose" and "expression" parameters to output images can be synthesized from a small set of example images and used to produce new images using a similar synthesis network. The techniques described here have several applications in computer graphics, special effects, interactive multimedia and very low bandwidth teleconferencing.

Digital image and video in their raw form require an enormous amount of storage capacity. Considering the important role played by digital imaging and video, it is necessary to develop a system that produces high degree of compression while preserving critical image/video information. There are various transformation techniques used for data compression. Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) are the most commonly used transformation. DCT has high energy compaction property and requires less computational resources. On the other hand, DWT is multiresolution transformation.<p> In this work, we propose a hybrid DWT-DCT algorithm for image compression and reconstruction taking benefit from the advantages of both algorithms. The algorithm performs the Discrete Cosine Transform (DCT) on the Discrete Wavelet Transform (DWT) coefficients. Simulations have been conducted on several natural, benchmark, medical and endoscopic images. Several QCIF, high definition, and endoscopic videos have also been used to demonstrate the advantage of the proposed scheme.<p> The simulation results show that the proposed hybrid DWT-DCT algorithm performs much better than the standalone JPEG-based DCT, DWT, and WHT algorithms in terms of peak signal to noise ratio (PSNR), as well as visual perception at higher compression ratio. The new scheme reduces false contouring and blocking artifacts significantly. The rate distortion analysis shows that for a fixed level of distortion, the number of bits required to transmit the hybrid coefficients would be less than those required for other schemes Furthermore, the proposed algorithm is also compared with the some existing hybrid algorithms. The comparison results show that, the proposed hybrid algorithm has better performance and reconstruction quality. The proposed scheme is intended to be used as the image/video compressor engine in imaging and video applications.

Aquesta recerca està centrada en l'escalabilitat qualitativa de l'estàndard de compressió d'imatges JPEG2000. L'escalabilitat qualitativa és una característica fonamental que permet el truncament de la tira de bits a diferents punts sense penalitzar la qualitat de la imatge recuperada. L'escalabilitat qualitativa és també fonamental en transmissions d'imatges interactives, ja que permet la transmissió de finestres d'interès a diferents qualitats. El JPEG2000 aconsegueix escalabilitat qualitativa a partir del mètode de control de factor de compressió utilitzat en el procés de compressió, que empotra capes de qualitat a la tira de bits. En alguns escenaris, aquesta arquitectura pot causar dos problemàtiques: per una banda, quan el procés de codificació acaba, el número i distribució de les capes de qualitat és permanent, causant una manca d'escalabilitat qualitativa a tires de bits amb una o poques capes de qualitat. Per altra banda, el mètode de control de factor de compressió construeix capes de qualitat considerant la optimització de la raó distorsió per l'àrea completa de la imatge, i això pot provocar que la distribució de les capes de qualitat per la transmissió de finestres d'interès no sigui adequada. Aquesta tesis introdueix tres mètodes de control de factor de compressió que proveeixen escalabilitat qualitativa per finestres d'interès, o per tota l'àrea de la imatge, encara que la tira de bits contingui una o poques capes de qualitat. El primer mètode està basat en una simple estratègia d'entrellaçat (CPI) que modela la raó distorsió a partir d'una aproximació clàssica. Un anàlisis acurat del CPI motiva el segon mètode, basat en un ordre d'escaneig invers i una concatenació de passades de codificació (ROC). El tercer mètode es beneficia dels models de raó distorsió del CPI i ROC, desenvolupant una novedosa aproximació basada en la caracterització de la raó distorsió dels blocs de codificació dins una subbanda (CoRD). Els resultats experimentals suggereixen que tant el CPI com el ROC són capaços de proporcionar escalabilitat qualitativa a tires de bits, encara que continguin una o poques capes de qualitat, aconseguint un rendiment de codificació pràcticament equivalent a l'obtingut amb l'ús de capes de qualitat. Tot i això, els resultats del CPI no estan ben balancejats per les diferents raons de compressió i el ROC presenta irregularitats segons el corpus d'imatges. CoRD millora els resultats de CPI i ROC i aconsegueix un rendiment ben balancejat. A més, CoRD obté un rendiment de compressió una mica millor que l'aconseguit amb l'ús de capes de qualitat. La complexitat computacional del CPI, ROC i CoRD és, a la pràctica, negligible, fent-los adequats per el seu ús en transmissions interactives d'imatges. / This work is focused on the quality scalability of the JPEG2000 image compression standard. Quality scalability is an important feature that allows the truncation of the code-stream at different bit-rates without penalizing the coding performance. Quality scalability is also fundamental in interactive image transmissions to allow the delivery of Windows of Interest (WOI) at increasing qualities. JPEG2000 achieves quality scalability through the rate control method used in the encoding process, which embeds quality layers to the code-stream. In some scenarios, this architecture might raise two drawbacks: on the one hand, when the coding process finishes, the number and bit-rates of quality layers are fixed, causing a lack of quality scalability to code-streams encoded with a single or few quality layers. On the other hand, the rate control method constructs quality layers considering the rate¬distortion optimization of the complete image, and this might not allocate the quality layers adequately for the delivery of a WOI at increasing qualities. This thesis introduces three rate control methods that supply quality scalability for WOIs, or for the complete image, even if the code-stream contains a single or few quality layers. The first method is based on a simple Coding Passes Interleaving (CPI) that models the rate-distortion through a classical approach. An accurate analysis of CPI motivates the second rate control method, which introduces simple modifications to CPI based on a Reverse subband scanning Order and coding passes Concatenation (ROC). The third method benefits from the rate-distortion models of CPI and ROC, developing an approach based on a novel Characterization of the Rate-Distortion slope (CoRD) that estimates the rate-distortion of the code¬blocks within a subband. Experimental results suggest that CPI and ROC are able to supply quality scalability to code-streams, even if they contain a single or few quality layers, achieving a coding performance almost equivalent to the one obtained with the use of quality layers. However, the results of CPI are unbalanced among bit-rates, and ROC presents an irregular coding performance for some corpus of images. CoRD outperforms CPI and ROC achieving well-balanced and regular results and, in addition, it obtains a slightly better coding performance than the one achieved with the use of quality layers. The computational complexity of CPI, ROC and CoRD is negligible in practice, making them suitable to control interactive image transmissions.

This thesis presents two novel coding schemes and applications to both two- and three-dimensional image compression. Image compression can be viewed as methods of functional approximation under a constraint on the amount of information allowable in specifying the approximation. Two methods of approximating functions are discussed: Iterated function systems (IFS) and wavelet-based approximations. IFS methods approximate a function by the fixed point of an iterated operator, using consequences of the Banach contraction mapping principle. Natural images under a wavelet basis have characteristic coefficient magnitude decays which may be used to aid approximation. The relationship between quantization, modelling, and encoding in a compression scheme is examined. Context based adaptive arithmetic coding is described. This encoding method is used in the coding schemes developed. A coder with explicit separation of the modelling and encoding roles is presented: an embedded wavelet bitplane coder based on hierarchical context in the wavelet coefficient trees. Fractal (spatial IFSM) and fractal-wavelet (coefficient tree), or IFSW, coders are discussed. A second coder is proposed, merging the IFSW approaches with the embedded bitplane coder. Performance of the coders, and applications to two- and three-dimensional images are discussed. Applications include two-dimensional still images in greyscale and colour, and three-dimensional streams (video).

Image compression is a means to perform transmission or storage of visual data in the most economical way. Though many algorithms have been reported, research is still needed to cope with the continuous demand for more efficient transmission or storage. This research work explores and implements polynomial fitting techniques as means to perform block-based lossy image compression. In an attempt to investigate nonpolynomial models, a region-based scheme is implemented to fit the whole image using bell-shaped functions. The idea is simply to view an image as a 3D geographical map consisting of hills and valleys. However, the scheme suffers from high computational demands and inferiority to many available image compression schemes. Hence, only polynomial models get further considerations. A first order polynomial (plane) model is designed to work in a multiplication- and division-free (MDF) environment. The intensity values of each image block are fitted to a plane and the parameters are then quantized and coded. Blocking artefacts, a common drawback of block-based image compression techniques, are reduced using an MDF line-fitting scheme at blocks’ boundaries. It is shown that a compression ratio of 62:1 at 28.8dB is attainable for the standard image PEPPER, outperforming JPEG, both objectively and subjectively for this part of the rate-distortion characteristics. Inter-block prediction can substantially improve the compression performance of the plane model to reach a compression ratio of 112:1 at 27.9dB. This improvement, however, slightly increases computational complexity and reduces pipelining capability. Although JPEG2000 is not a block-based scheme, it is encouraging that the proposed prediction scheme performs better in comparison to JPEG 2000, computationally and qualitatively. However, more experiments are needed to have a more concrete comparison. To reduce blocking artefacts, a new postprocessing scheme, based on Weber’s law, is employed. It is reported that images postprocessed using this scheme are subjectively more pleasing with a marginal increase in PSNR (<0.3 dB). The Weber’s law is modified to perform edge detection and quality assessment tasks. These results motivate the exploration of higher order polynomials, using three parameters to maintain comparable compression performance. To investigate the impact of higher order polynomials, through an approximate asymptotic behaviour, a novel linear mapping scheme is designed. Though computationally demanding, the performances of higher order polynomial approximation schemes are comparable to that of the plane model. This clearly demonstrates the powerful approximation capability of the plane model. As such, the proposed linear mapping scheme constitutes a new approach in image modeling, and hence worth future consideration.

In digital watermarking, one embeds a watermark into a covertext, in such a way that the resulting watermarked signal is robust to a certain distortion caused by either standard data processing in a friendly environment or malicious attacks in an unfriendly environment. In addition to the robustness, there are two other conflicting requirements a good watermarking system should meet: one is referred as perceptual quality, that is, the distortion incurred to the original signal should be small; and the other is payload, the amount of information embedded (embedding rate) should be as high as possible. To a large extent, digital watermarking is a science and/or art aiming to design watermarking systems meeting these three conflicting requirements. As watermarked signals are highly desired to be compressed in real world applications, we have looked into the design and analysis of joint watermarking and compression (JWC) systems to achieve efficient tradeoffs among the embedding rate, compression rate, distortion and robustness. Using variable-rate scalar quantization, an optimum encoding and decoding scheme for JWC systems is designed and analyzed to maximize the robustness in the presence of additive Gaussian attacks under constraints on both compression distortion and composite rate. Simulation results show that in comparison with the previous work of designing JWC systems using fixed-rate scalar quantization, optimum JWC systems using variable-rate scalar quantization can achieve better performance in the distortion-to-noise ratio region of practical interest. Inspired by the good performance of JWC systems, we then investigate its applications in image compression. We look into the design of a joint image compression and blind watermarking system to maximize the compression rate-distortion performance while maintaining baseline JPEG decoder compatibility and satisfying the additional constraints imposed by watermarking. Two watermarking embedding schemes, odd-even watermarking (OEW) and zero-nonzero watermarking (ZNW), have been proposed for the robustness to a class of standard JPEG recompression attacks. To maximize the compression performance, two corresponding alternating algorithms have been developed to jointly optimize run-length coding, Huffman coding and quantization table selection subject to the additional constraints imposed by OEW and ZNW respectively. Both of two algorithms have been demonstrated to have better compression performance than the DQW and DEW algorithms developed in the recent literature. Compared with OEW scheme, the ZNW embedding method sacrifices some payload but earns more robustness against other types of attacks. In particular, the zero-nonzero watermarking scheme can survive a class of valumetric distortion attacks including additive noise, amplitude changes and recompression for everyday usage.

This thesis presents two novel coding schemes and applications to both two- and three-dimensional image compression. Image compression can be viewed as methods of functional approximation under a constraint on the amount of information allowable in specifying the approximation. Two methods of approximating functions are discussed: Iterated function systems (IFS) and wavelet-based approximations. IFS methods approximate a function by the fixed point of an iterated operator, using consequences of the Banach contraction mapping principle. Natural images under a wavelet basis have characteristic coefficient magnitude decays which may be used to aid approximation. The relationship between quantization, modelling, and encoding in a compression scheme is examined. Context based adaptive arithmetic coding is described. This encoding method is used in the coding schemes developed. A coder with explicit separation of the modelling and encoding roles is presented: an embedded wavelet bitplane coder based on hierarchical context in the wavelet coefficient trees. Fractal (spatial IFSM) and fractal-wavelet (coefficient tree), or IFSW, coders are discussed. A second coder is proposed, merging the IFSW approaches with the embedded bitplane coder. Performance of the coders, and applications to two- and three-dimensional images are discussed. Applications include two-dimensional still images in greyscale and colour, and three-dimensional streams (video).

Image compression is a means to perform transmission or storage of visual data in the most economical way. Though many algorithms have been reported, research is still needed to cope with the continuous demand for more efficient transmission or storage. This research work explores and implements polynomial fitting techniques as means to perform block-based lossy image compression. In an attempt to investigate nonpolynomial models, a region-based scheme is implemented to fit the whole image using bell-shaped functions. The idea is simply to view an image as a 3D geographical map consisting of hills and valleys. However, the scheme suffers from high computational demands and inferiority to many available image compression schemes. Hence, only polynomial models get further considerations. A first order polynomial (plane) model is designed to work in a multiplication- and division-free (MDF) environment. The intensity values of each image block are fitted to a plane and the parameters are then quantized and coded. Blocking artefacts, a common drawback of block-based image compression techniques, are reduced using an MDF line-fitting scheme at blocks’ boundaries. It is shown that a compression ratio of 62:1 at 28.8dB is attainable for the standard image PEPPER, outperforming JPEG, both objectively and subjectively for this part of the rate-distortion characteristics. Inter-block prediction can substantially improve the compression performance of the plane model to reach a compression ratio of 112:1 at 27.9dB. This improvement, however, slightly increases computational complexity and reduces pipelining capability. Although JPEG2000 is not a block-based scheme, it is encouraging that the proposed prediction scheme performs better in comparison to JPEG 2000, computationally and qualitatively. However, more experiments are needed to have a more concrete comparison. To reduce blocking artefacts, a new postprocessing scheme, based on Weber’s law, is employed. It is reported that images postprocessed using this scheme are subjectively more pleasing with a marginal increase in PSNR (<0.3 dB). The Weber’s law is modified to perform edge detection and quality assessment tasks. These results motivate the exploration of higher order polynomials, using three parameters to maintain comparable compression performance. To investigate the impact of higher order polynomials, through an approximate asymptotic behaviour, a novel linear mapping scheme is designed. Though computationally demanding, the performances of higher order polynomial approximation schemes are comparable to that of the plane model. This clearly demonstrates the powerful approximation capability of the plane model. As such, the proposed linear mapping scheme constitutes a new approach in image modeling, and hence worth future consideration.

In digital watermarking, one embeds a watermark into a covertext, in such a way that the resulting watermarked signal is robust to a certain distortion caused by either standard data processing in a friendly environment or malicious attacks in an unfriendly environment. In addition to the robustness, there are two other conflicting requirements a good watermarking system should meet: one is referred as perceptual quality, that is, the distortion incurred to the original signal should be small; and the other is payload, the amount of information embedded (embedding rate) should be as high as possible. To a large extent, digital watermarking is a science and/or art aiming to design watermarking systems meeting these three conflicting requirements. As watermarked signals are highly desired to be compressed in real world applications, we have looked into the design and analysis of joint watermarking and compression (JWC) systems to achieve efficient tradeoffs among the embedding rate, compression rate, distortion and robustness. Using variable-rate scalar quantization, an optimum encoding and decoding scheme for JWC systems is designed and analyzed to maximize the robustness in the presence of additive Gaussian attacks under constraints on both compression distortion and composite rate. Simulation results show that in comparison with the previous work of designing JWC systems using fixed-rate scalar quantization, optimum JWC systems using variable-rate scalar quantization can achieve better performance in the distortion-to-noise ratio region of practical interest. Inspired by the good performance of JWC systems, we then investigate its applications in image compression. We look into the design of a joint image compression and blind watermarking system to maximize the compression rate-distortion performance while maintaining baseline JPEG decoder compatibility and satisfying the additional constraints imposed by watermarking. Two watermarking embedding schemes, odd-even watermarking (OEW) and zero-nonzero watermarking (ZNW), have been proposed for the robustness to a class of standard JPEG recompression attacks. To maximize the compression performance, two corresponding alternating algorithms have been developed to jointly optimize run-length coding, Huffman coding and quantization table selection subject to the additional constraints imposed by OEW and ZNW respectively. Both of two algorithms have been demonstrated to have better compression performance than the DQW and DEW algorithms developed in the recent literature. Compared with OEW scheme, the ZNW embedding method sacrifices some payload but earns more robustness against other types of attacks. In particular, the zero-nonzero watermarking scheme can survive a class of valumetric distortion attacks including additive noise, amplitude changes and recompression for everyday usage.