Context-based bit plane golomb coder for scalable image coding

Master'sMASTER OF ENGINEERIN

Efficient compression of motion compensated residuals

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A low complexity image compression algorithm for Bayer color filter array

Digital image in their raw form requires an excessive amount of storage capacity. Image compression is a process of reducing the cost of storage and transmission of image data. The compression algorithm reduces the file size so that it requires less storage or transmission bandwidth. This work presents a new color transformation and compression algorithm for the Bayer color filter array (CFA) images. In a full color image, each pixel contains R, G, and B components. A CFA image contains single channel information in each pixel position, demosaicking is required to construct a full color image. For each pixel, demosaicking constructs the missing two-color information by using information from neighbouring pixels. After demosaicking, each pixel contains R, G, and B information, and a full color image is constructed. Conventional CFA compression occurs after the demosaicking. However, the Bayer CFA image can be compressed before demosaicking which is called compression-first method, and the algorithm proposed in this research follows the compression-first or direct compression method. The compression-first method applies the compression algorithm directly onto the CFA data and shifts demosaicking to the other end of the transmission and storage process. The advantage of the compression-first method is that it requires three time less transmission bandwidth for each pixel than conventional compression. Compression-first method of CFA data produces spatial redundancy, artifacts, and false high frequencies. The process requires a color transformation with less correlation among the color components than that Bayer RGB color space. This work analyzes correlation coefficient, standard deviation, entropy, and intensity range of the Bayer RGB color components. The analysis provides two efficient color transformations in terms of features of color transformation. The proposed color components show lesser correlation coefficient than occurs with the Bayer RGB color components. Color transformations reduce both the spatial and spectral redundancies of the Bayer CFA image. After color transformation, the components are independently encoded using differential pulse-code modulation (DPCM) in raster order fashion. The residue error of DPCM is mapped to a positive integer for the adaptive Golomb rice code. The compression algorithm includes both the adaptive Golomb rice and Unary coding to generate bit stream. Extensive simulation analysis is performed on both simulated CFA and real CFA datasets. This analysis is extended for the WCE (wireless capsule endoscopic) images. The compression algorithm is also realized with a simulated WCE CFA dataset. The results show that the proposed algorithm requires less bits per pixel than the conventional CFA compression. The algorithm also outperforms recent works on CFA compression algorithms for both real and simulated CFA datasets

Virtually Lossless Compression of Astrophysical Images

We describe an image compression strategy potentially capable of preserving the scientific quality of astrophysical data, simultaneously allowing a consistent bandwidth reduction to be achieved. Unlike strictly lossless techniques, by which moderate compression ratios are attainable, and conventional lossy techniques, in which the mean square error of the decoded data is globally controlled by users, near-lossless methods are capable of locally constraining the maximum absolute error, based on user's requirements. An advanced lossless/near-lossless differential pulse code modulation (DPCM) scheme, recently introduced by the authors and relying on a causal spatial prediction, is adjusted to the specific characteristics of astrophysical image data (high radiometric resolution, generally low noise, etc.). The background noise is preliminarily estimated to drive the quantization stage for high quality, which is the primary concern in most of astrophysical applications. Extensive experimental results of lossless, near-lossless, and lossy compression of astrophysical images acquired by the Hubble space telescope show the advantages of the proposed method compared to standard techniques like JPEG-LS and JPEG2000. Eventually, the rationale of virtually lossless compression, that is, a noise-adjusted lossles/near-lossless compression, is highlighted and found to be in accordance with concepts well established for the astronomers' community

Efficient Coding of Transform Coefficient Levels in Hybrid Video Coding

All video coding standards of practical importance, such as Advanced Video Coding (AVC), its successor High Efficiency Video Coding (HEVC), and the state-of-the-art Versatile Video Coding (VVC), follow the basic principle of block-based hybrid video coding. In such an architecture, the video pictures are partitioned into blocks. Each block is first predicted by either intra-picture or motion-compensated prediction, and the resulting prediction errors, referred to as residuals, are compressed using transform coding. This thesis deals with the entropy coding of quantization indices for transform coefficients, also referred to as transform coefficient levels, as well as the entropy coding of directly quantized residual samples. The entropy coding of quantization indices is referred to as level coding in this thesis. The presented developments focus on both improving the coding efficiency and reducing the complexity of the level coding for HEVC and VVC. These goals were achieved by modifying the context modeling and the binarization of the level coding. The first development presented in this thesis is a transform coefficient level coding for variable transform block sizes, which was introduced in HEVC. It exploits the fact that non-zero levels are typically concentrated in certain parts of the transform block by partitioning blocks larger than \square{4} samples into \square{4} sub-blocks. Each \square{4} sub-block is then coded similarly to the level coding specified in AVC for \square{4} transform blocks. This sub-block processing improves coding efficiency and has the advantage that the number of required context models is independent of the set of supported transform block sizes. The maximum number of context-coded bins for a transform coefficient level is one indicator for the complexity of the entropy coding. An adaptive binarization of absolute transform coefficient levels using Rice codes is presented that reduces the maximum number of context-coded bins from 15 (as used in AVC) to three for HEVC. Based on the developed selection of an appropriate Rice code for each scanning position, this adaptive binarization achieves virtually the same coding efficiency as the binarization specified in AVC for bit-rate operation points typically used in consumer applications. The coding efficiency is improved for high bit-rate operation points, which are used in more advanced and professional applications. In order to further improve the coding efficiency for HEVC and VVC, the statistical dependencies among the transform coefficient levels of a transform block are exploited by a template-based context modeling developed in this thesis. Instead of selecting the context model for a current scanning position primarily based on its location inside a transform block, already coded neighboring locations inside a local template are utilized. To further increase the coding efficiency achieved by the template-based context modeling, the different coding phases of the initially developed level coding are merged into a single coding phase. As a consequence, the template-based context modeling can utilize the absolute levels of the neighboring frequency locations, which provides better conditional probability estimates and further improves coding efficiency. This template-based context modeling with a single coding phase is also suitable for trellis-coded quantization (TCQ), since TCQ is state-driven and derives the next state from the current state and the parity of the current level. TCQ introduces different context model sets for coding the significance flag depending on the current state. Based on statistical analyses, an extension of the state-dependent context modeling of TCQ is presented, which further improves the coding efficiency in VVC. After that, a method to reduce the complexity of the level coding at the decoder is presented. This method separates the level coding into a coding phase exclusively consisting of context-coded bins and another one consisting of bypass-coded bins only. For retaining the state-dependent context selection, which significantly contributes to the coding efficiency of TCQ, a dedicated parity flag is introduced and coded with context models in the first coding phase. An adaptive approach is then presented that further reduces the worst-case complexity, effectively lowering the maximum number of context-coded bins per transform coefficient to 1.75 without negatively affecting the coding efficiency. In the last development presented in this thesis, a dedicated level coding for transform skip blocks, which often occur in screen content applications, is introduced for VVC. This dedicated level coding better exploits the statistical properties of directly quantized residual samples for screen content. Various modifications to the level coding improve the coding efficiency for this type of content. Examples for these modifications are a binarization with additional context-coded flags and the coding of the sign information with adaptive context models

High-performance hardware accelerators for image processing in space applications

Mars is a hard place to reach. While there have been many notable success stories in getting probes to the Red Planet, the historical record is full of bad news. The success rate for actually landing on the Martian surface is even worse, roughly 30%. This low success rate must be mainly credited to the Mars environment characteristics. In the Mars atmosphere strong winds frequently breath. This phenomena usually modifies the lander descending trajectory diverging it from the target one. Moreover, the Mars surface is not the best place where performing a safe land. It is pitched by many and close craters and huge stones, and characterized by huge mountains and hills (e.g., Olympus Mons is 648 km in diameter and 27 km tall). For these reasons a mission failure due to a landing in huge craters, on big stones or on part of the surface characterized by a high slope is highly probable. In the last years, all space agencies have increased their research efforts in order to enhance the success rate of Mars missions. In particular, the two hottest research topics are: the active debris removal and the guided landing on Mars. The former aims at finding new methods to remove space debris exploiting unmanned spacecrafts. These must be able to autonomously: detect a debris, analyses it, in order to extract its characteristics in terms of weight, speed and dimension, and, eventually, rendezvous with it. In order to perform these tasks, the spacecraft must have high vision capabilities. In other words, it must be able to take pictures and process them with very complex image processing algorithms in order to detect, track and analyse the debris. The latter aims at increasing the landing point precision (i.e., landing ellipse) on Mars. Future space-missions will increasingly adopt Video Based Navigation systems to assist the entry, descent and landing (EDL) phase of space modules (e.g., spacecrafts), enhancing the precision of automatic EDL navigation systems. For instance, recent space exploration missions, e.g., Spirity, Oppurtunity, and Curiosity, made use of an EDL procedure aiming at following a fixed and precomputed descending trajectory to reach a precise landing point. This approach guarantees a maximum landing point precision of 20 km. By comparing this data with the Mars environment characteristics, it is possible to understand how the mission failure probability still remains really high. A very challenging problem is to design an autonomous-guided EDL system able to even more reduce the landing ellipse, guaranteeing to avoid the landing in dangerous area of Mars surface (e.g., huge craters or big stones) that could lead to the mission failure. The autonomous behaviour of the system is mandatory since a manual driven approach is not feasible due to the distance between Earth and Mars. Since this distance varies from 56 to 100 million of km approximately due to the orbit eccentricity, even if a signal transmission at the light speed could be possible, in the best case the transmission time would be around 31 minutes, exceeding so the overall duration of the EDL phase. In both applications, algorithms must guarantee self-adaptability to the environmental conditions. Since the Mars (and in general the space) harsh conditions are difficult to be predicted at design time, these algorithms must be able to automatically tune the internal parameters depending on the current conditions. Moreover, real-time performances are another key factor. Since a software implementation of these computational intensive tasks cannot reach the required performances, these algorithms must be accelerated via hardware. For this reasons, this thesis presents my research work done on advanced image processing algorithms for space applications and the associated hardware accelerators. My research activity has been focused on both the algorithm and their hardware implementations. Concerning the first aspect, I mainly focused my research effort to integrate self-adaptability features in the existing algorithms. While concerning the second, I studied and validated a methodology to efficiently develop, verify and validate hardware components aimed at accelerating video-based applications. This approach allowed me to develop and test high performance hardware accelerators that strongly overcome the performances of the actual state-of-the-art implementations. The thesis is organized in four main chapters. Chapter 2 starts with a brief introduction about the story of digital image processing. The main content of this chapter is the description of space missions in which digital image processing has a key role. A major effort has been spent on the missions in which my research activity has a substantial impact. In particular, for these missions, this chapter deeply analizes and evaluates the state-of-the-art approaches and algorithms. Chapter 3 analyzes and compares the two technologies used to implement high performances hardware accelerators, i.e., Application Specific Integrated Circuits (ASICs) and Field Programmable Gate Arrays (FPGAs). Thanks to this information the reader may understand the main reasons behind the decision of space agencies to exploit FPGAs instead of ASICs for high-performance hardware accelerators in space missions, even if FPGAs are more sensible to Single Event Upsets (i.e., transient error induced on hardware component by alpha particles and solar radiation in space). Moreover, this chapter deeply describes the three available space-grade FPGA technologies (i.e., One-time Programmable, Flash-based, and SRAM-based), and the main fault-mitigation techniques against SEUs that are mandatory for employing space-grade FPGAs in actual missions. Chapter 4 describes one of the main contribution of my research work: a library of high-performance hardware accelerators for image processing in space applications. The basic idea behind this library is to offer to designers a set of validated hardware components able to strongly speed up the basic image processing operations commonly used in an image processing chain. In other words, these components can be directly used as elementary building blocks to easily create a complex image processing system, without wasting time in the debug and validation phase. This library groups the proposed hardware accelerators in IP-core families. The components contained in a same family share the same provided functionality and input/output interface. This harmonization in the I/O interface enables to substitute, inside a complex image processing system, components of the same family without requiring modifications to the system communication infrastructure. In addition to the analysis of the internal architecture of the proposed components, another important aspect of this chapter is the methodology used to develop, verify and validate the proposed high performance image processing hardware accelerators. This methodology involves the usage of different programming and hardware description languages in order to support the designer from the algorithm modelling up to the hardware implementation and validation. Chapter 5 presents the proposed complex image processing systems. In particular, it exploits a set of actual case studies, associated with the most recent space agency needs, to show how the hardware accelerator components can be assembled to build a complex image processing system. In addition to the hardware accelerators contained in the library, the described complex system embeds innovative ad-hoc hardware components and software routines able to provide high performance and self-adaptable image processing functionalities. To prove the benefits of the proposed methodology, each case study is concluded with a comparison with the current state-of-the-art implementations, highlighting the benefits in terms of performances and self-adaptability to the environmental conditions

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Graph-based compression of dynamic 3D point cloud sequences

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames are similar, motion estimation is key to effective compression of these sequences. It however remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the points clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way

A practical comparison between two powerful PCC codec’s

Recent advances in the consumption of 3D content creates the necessity of efficient ways to visualize and transmit 3D content. As a result, methods to obtain that same content have been evolving, leading to the development of new methods of representations, namely point clouds and light fields. A point cloud represents a set of points with associated Cartesian coordinates associated with each point(x, y, z), as well as being able to contain even more information inside that point (color, material, texture, etc). This kind of representation changes the way on how 3D content in consumed, having a wide range of applications, from videogaming to medical ones. However, since this type of data carries so much information within itself, they are data-heavy, making the storage and transmission of content a daunting task. To resolve this issue, MPEG created a point cloud coding normalization project, giving birth to V-PCC (Video-based Point Cloud Coding) and G-PCC (Geometry-based Point Cloud Coding) for static content. Firstly, a general analysis of point clouds is made, spanning from their possible solutions, to their acquisition. Secondly, point cloud codecs are studied, namely VPCC and G-PCC from MPEG. Then, a state of art study of quality evaluation is performed, namely subjective and objective evaluation. Finally, a report on the JPEG Pleno Point Cloud, in which an active colaboration took place, is made, with the comparative results of the two codecs and used metrics.Os avanços recentes no consumo de conteúdo 3D vêm criar a necessidade de maneiras eficientes de visualizar e transmitir conteúdo 3D. Consequentemente, os métodos de obtenção desse mesmo conteúdo têm vindo a evoluir, levando ao desenvolvimento de novas maneiras de representação, nomeadamente point clouds e lightfields. Um point cloud (núvem de pontos) representa um conjunto de pontos com coordenadas cartesianas associadas a cada ponto (x, y, z), além de poder conter mais informação dentro do mesmo (cor, material, textura, etc). Este tipo de representação abre uma nova janela na maneira como se consome conteúdo 3D, tendo um elevado leque de aplicações, desde videojogos e realidade virtual a aplicações médicas. No entanto, este tipo de dados, ao carregarem com eles tanta informação, tornam-se incrivelmente pesados, tornando o seu armazenamento e transmissão uma tarefa hercúleana. Tendo isto em mente, a MPEG criou um projecto de normalização de codificação de point clouds, dando origem ao V-PCC (Video-based Point Cloud Coding) e G-PCC (Geometry-based Point Cloud Coding) para conteúdo estático. Esta dissertação tem como objectivo uma análise geral sobre os point clouds, indo desde as suas possívei utilizações à sua aquisição. Seguidamente, é efectuado um estudo dos codificadores de point clouds, nomeadamente o V-PCC e o G-PCC da MPEG, o estado da arte da avaliação de qualidade, objectiva e subjectiva, e finalmente, são reportadas as actividades da JPEG Pleno Point Cloud, na qual se teve uma colaboração activa

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems