Grayscale and colour image Codec based on matching pursuit in the spatio-frequency domain

This report presents and evaluates a novel idea for scalable lossy colour image coding with Matching Pursuit (MP) performed in a transform domain. The benefits of the idea of MP performed in the transform domain are analysed in detail. The main contribution of this work is extending MP with wavelets to colour coding and proposing a coding method. We exploit correlations between image subbands after wavelet transformation in RGB colour space. Then, a new and simple quantisation and coding scheme of colour MP decomposition based on Run Length Encoding (RLE), inspired by the idea of coding indexes in relational databases, is applied. As a final coding step arithmetic coding is used assuming uniform distributions of MP atom parameters. The target application is compression at low and medium bit-rates. Coding performance is compared to JPEG 2000 showing the potential to outperform the latter with more sophisticated than uniform data models for arithmetic coder. The results are presented for grayscale and colour coding of 12 standard test images

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

Rate distortion control in digital video coding

Lossy compression is widely applied for coding visual information in applications such as entertainment in order to achieve a high compression ratio. In this case, the video quality worsens as the compression ratio increases. Rate control tries to use the bit budget properly so the visual distortion is minimized. Rate control for H.264, the state-of-the-art hybrid video coder, is investigated. Based on the Rate-Distortion (R-D) slope analysis, an operational rate distortion optimization scheme for H.264 using Lagrangian multiplier method is proposed. The scheme tries to find the best path of quantization parameter (OP) options at each macroblock. The proposed scheme provides a smoother rate control that is able to cover a wider range of bit rates and for many sequences it outperforms the H.264 (JM92 version) rate control scheme in the sense of PSNR. The Bath University Matching Pursuit (BUMP) project develops a new matching pursuit (MP) technique as an alternative to transform video coders. By combining MP with precision limited quantization (PLO) and multi-pass embedded residual group encoder (MERGE), a very efficient coder is built that is able to produce an embedded bit stream, which is highly desirable for rate control. The problem of optimal bit allocation with a BUMP based video coder is investigated. An ad hoc scheme of simply limiting the maximum atom number shows an obvious performance improvement, which indicates a potential of efficiency improvement. An in depth study on the bit Rate-Atom character has been carried out and a rate estimation model has been proposed. The model gives a theoretical description of how the oit number changes. An adaptive rate estimation algorithm has been proposed. Experiments show that the algorithm provides extremely high estimation accuracy. The proposed R-D source model is then applied to bit allocation in the BUMP based video coder. An R-D slope unifying scheme was applied to optimize the performance of the coder'. It adopts the R-D model and fits well within the BUMP coder. The optimization can be performed in a straightforward way. Experiments show that the proposed method greatly improved performance of BUMP video coder, and outperforms H.264 in low and medium bit rates by up to 2 dB.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

Support Recovery of Sparse Signals

We consider the problem of exact support recovery of sparse signals via noisy measurements. The main focus is the sufficient and necessary conditions on the number of measurements for support recovery to be reliable. By drawing an analogy between the problem of support recovery and the problem of channel coding over the Gaussian multiple access channel, and exploiting mathematical tools developed for the latter problem, we obtain an information theoretic framework for analyzing the performance limits of support recovery. Sharp sufficient and necessary conditions on the number of measurements in terms of the signal sparsity level and the measurement noise level are derived. Specifically, when the number of nonzero entries is held fixed, the exact asymptotics on the number of measurements for support recovery is developed. When the number of nonzero entries increases in certain manners, we obtain sufficient conditions tighter than existing results. In addition, we show that the proposed methodology can deal with a variety of models of sparse signal recovery, hence demonstrating its potential as an effective analytical tool.Comment: 33 page

Projection-Based and Look Ahead Strategies for Atom Selection

In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look ahead strategy requires a higher computational resource. To achieve a trade-off between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the proposed algorithms with existing greedy search and convex relaxation algorithms.Comment: sparsity, compressive sensing; IEEE Trans on Signal Processing 201

Evolutionary Multiresolution Matching Pursuit and its Relations with the Human Visual System

This paper proposes a multiresolution Matching Pursuit decomposition of natural images. Matching Pursuit is a greedy algorithm that decomposes any signal into a linear expansion of waveforms taken from a redundant dictionary, by iteratively picking the waveform that best matches the input signal. Since the computational cost rapidly grows with the size of the signal, we propose a multiresolution strategy that, together with an efficient dictionary, significantly reduces the encoding complexity while still providing an efficient representation. Such a decomposition is perceptually very effective at low bit rate coding, thanks to similiarities with the Human Visual System information processing