Rate scalable image compression in the wavelet domain

This thesis explores image compression in the wavelet transform domain. This the- sis considers progressive compression based on bit plane coding. The rst part of the thesis investigates the scalar quantisation technique for multidimensional images such as colour and multispectral image. Embedded coders such as SPIHT and SPECK are known to be very simple and e cient algorithms for compression in the wavelet do- main. However, these algorithms require the use of lists to keep track of partitioning processes, and such lists involve high memory requirement during the encoding process. A listless approach has been proposed for multispectral image compression in order to reduce the working memory required. The earlier listless coders are extended into three dimensional coder so that redundancy in the spectral domain can be exploited. Listless implementation requires a xed memory of 4 bits per pixel to represent the state of each transformed coe cient. The state is updated during coding based on test of sig- ni cance. Spectral redundancies are exploited to improve the performance of the coder by modifying its scanning rules and the initial marker/state. For colour images, this is done by conducting a joint the signi cant test for the chrominance planes. In this way, the similarities between the chrominance planes can be exploited during the cod- ing process. Fixed memory listless methods that exploit spectral redundancies enable e cient coding while maintaining rate scalability and progressive transmission. The second part of the thesis addresses image compression using directional filters in the wavelet domain. A directional lter is expected to improve the retention of edge and curve information during compression. Current implementations of hybrid wavelet and directional (HWD) lters improve the contour representation of compressed images, but su er from the pseudo-Gibbs phenomenon in the smooth regions of the images. A di erent approach to directional lters in the wavelet transforms is proposed to remove such artifacts while maintaining the ability to preserve contours and texture. Imple- mentation with grayscale images shows improvements in terms of distortion rates and the structural similarity, especially in images with contours. The proposed transform manages to preserve the directional capability without pseudo-Gibbs artifacts and at the same time reduces the complexity of wavelet transform with directional lter. Fur-ther investigation to colour images shows the transform able to preserve texture and curve.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Robust light field watermarking by 4D wavelet transform

Unlike common 2D images, the light field representation of a scene delivers spatial and angular description which is of paramount importance for 3D reconstruction. Despite the numerous methods proposed for 2D image watermarking, such methods do not address the angular information of the light field. Hence the exploitation of such methods may cause severe destruction of the angular information. In this paper, we propose a novel method for light field watermarking with extensive consideration of the spatial and angular information. Considering the 4D innate of the light field, the proposed method incorporates 4D wavelet for the purpose of watermarking and converts the heavily-correlated channels from RGB domain to YUV. The robustness of the proposed method has been evaluated against common image processing attacks

Iterative enhanced multivariance products representation for effective compression of hyperspectral images.

Effective compression of hyperspectral (HS) images is essential due to their large data volume. Since these images are high dimensional, processing them is also another challenging issue. In this work, an efficient lossy HS image compression method based on enhanced multivariance products representation (EMPR) is proposed. As an efficient data decomposition method, EMPR enables us to represent the given multidimensional data with lower-dimensional entities. EMPR, as a finite expansion with relevant approximations, can be acquired by truncating this expansion at certain levels. Thus, EMPR can be utilized as a highly effective lossy compression algorithm for hyper spectral images. In addition to these, an efficient variety of EMPR is also introduced in this article, in order to increase the compression efficiency. The results are benchmarked with several state-of-the-art lossy compression methods. It is observed that both higher peak signal-to-noise ratio values and improved classification accuracy are achieved from EMPR-based methods

Processing of Multichannel Remote-Sensing Images with Prediction of Performance Parameters

In processing of multichannel remote sensing data, there is a need in automation of basic operations as filtering and compression. Automation presumes undertaking a decision on expedience of image filtering. Automation also deals with obtaining of information based on which certain decisions can be undertaken or parameters of processing algorithms can be chosen. For the considered operations of denoising and lossy compression, it is shown that their basic performance characteristics can be quite easily predicted based on easily calculated local statistics in discrete cosine transform (DCT) domain. The described methodology of prediction is shown to be general and applicable to different types of noise under condition that its basic characteristics are known in advance or pre-estimated accurately

Summative Stereoscopic Image Compression using Arithmetic Coding

Image compression targets at plummeting the amount of bits required for image representation for save storage space and speed up the transmission over network. The reduction of size helps to store more images in the disk and take less transfer time in the data network. Stereoscopic image refers to a three dimensional (3D) image that is perceived by the human brain as the transformation of two images that is being sent to the left and right human eyes with distinct phases. However, storing of these images takes twice space than a single image and hence the motivation for this novel approach called Summative Stereoscopic Image Compression using Arithmetic Coding (S2ICAC) where the difference and average of these stereo pair images are calculated, quantized in the case of lossy approach and unquantized in the case of lossless approach, and arithmetic coding is applied. The experimental result analysis indicates that the proposed method achieves high compression ratio and high PSNR value. The proposed method is also compared with JPEG 2000 Position Based Coding Scheme(JPEG 2000 PBCS) and Stereoscopic Image Compression using Huffman Coding (SICHC). From the experimental analysis, it is observed that S2ICAC outperforms JPEG 2000 PBCS as well as SICHC

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Light field image processing: an overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data

Clustered-dot periodic halftone screen design and ICC profile color table compression

This dissertation studies image quality problems associated with rendering images in devices like printing or displaying. It mainly includes two parts: clustered-dot periodic halftone screen design, and color table compression. Screening is a widely used halftoning method. As a consequence of the lower resolution of digital presses and printers, the number of printer-addressable dots or holes in each microcell may be too few to provide the requisite number of tone lev- els between paper white and full colorant coverage. To address this limitation, the microcells can be grouped into supercells. The challenge is then to determine the desired supercell shape and the order in which dots are added to the microcell. Using DBS to determine this order results in a very homogeneous halftone pattern. To simplify the design and implementation of supercell halftone screens, it is common to repeat the supercell to yield a periodically repeating rectangular block called the basic screen block (BSB). While applying DBS to design a dot-cluster growth order- ing for the entire BSB is simpler to implement than is the application of DBS to the single non-rectangular supercell, it is computationally very inefficient. To achieve a more efficient way to apply DBS to determine the microcell sequence, we describe a procedure for design of high-quality regular screens using the non-rectangular super- cell. A novel concept the Elementary Periodicity Set is proposed to characterize how a supercell is developed. After a supercell is set, we use DBS to determine the micro-cell sequence within the supercell. We derive the DBS equations for this situation, and show that it is more efficient to implement. Then, we mainly focus on the regular and irregular screen design. With digital printing systems, the achievable screen angles and frequencies are limited by the finite addressability of the marking engine. In order for such screens to generate dot clusters in which each cluster is identical, the elements of the periodicity matrix must be integer-valued, when expressed in units of printer-addressable pixels. Good approximation of the screen sets result in better printing quality. So to achieve a better approximation to the screen sets used for commercial offset printing, irregular screens can be used. With an irregular screen, the elements of the periodicity matrix are rational numbers. In this section, first we propose a procedure to generate regular screens starting from midtone level. And then we describe a procedure for design of high-quality irregular screens based on the regular screen design method. We then propose an algorithm to determine how to add dots from midtone to shadow and how to remove dots from midtone to highlight. We present experimental results illustrating the quality of the halftones resulting from our design procedure by comparing images halftoned with irregular screens using our approach and a template-based approach. We also present the evaluation of the smoothness and improvement of the proposed methods. In the next part, we study another quality problem: ICC profile color table compression. ICC profiles are widely used to provide transformations between different color spaces in different devices. The color look-up tables (CLUTs) in the profiles will increase the file sizes when embedded in color documents. In this chapter, we discuss compression methods that decrease the storage cost of the CLUTs. For DCT compression method, a compressed color table includes quantized DCT coefficients for the color table, the additional nodes with large color difference, and the coefficients bit assignment table. For wavelet-based compression method, a compressed color table includes output of the wavelet encoding method, and the additional nodes with large color difference. These methods support lossy table compression to minimize the network traffic and delay, and also achieves relatively small maximum color difference