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You searched for subject:(signal decorrelation). Showing records 1 – 3 of 3 total matches.

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University of Southern California

1. Dai, Yunyang. Advanced intra prediction techniques for image and video coding.

Degree: PhD, Electrical Engineering, 2010, University of Southern California

Intra prediction has been used in the H.264/AVC video coding standard to improve the coding efficiency of the intra frame. We present different intra prediction techniques that outperform the existing ones adopted by H.264/AVC and JPEG-LS in this research: 1. joint block/line-based intra prediction (JBLIP), 2. hierarchical (or multi-resolution) intra prediction (HIP), and 3. context-based hierarchical intra prediction (CHIP).; We consider two image/video coding scenarios: lossy compression and lossless compression. For lossy compression, we conduct a comprehensive study and show that the existing line-based prediction (LIP) technique adopted by the H.264/AVC standard can only be effective in smooth and simple edge regions. However, it is not as useful in predicting complex regions that contain texture patterns. To overcome this difficulty, we propose a JBLIP scheme with 2D geometrical manipulation to improve coding efficiency. The complexity of the JBLIP scheme is however quite hight due to the need to search the best matched block for the prediction purpose. Thus, we propose a fast search algorithm to reduce the coding complexity. The proposed JBLIP scheme outperforms the LIP scheme in H.264/AVC by up to 1.68dB in the PSNR improvement at the same bit rate.; Next, for lossless compression, we present an advanced intra frame coding using a hierarchical (or multi-resolution) approach called HIP. The objective is to support lossless image/video compression with spatial scalability. We analyze the characteristics of the underlying input signal characteristics and previously proposed signal modeling algorithms and show that most of the existing signal models cannot capture the dynamic signal characteristics through one fixed model. Hence, we propose a spatially scalable intra-prediction scheme that decompose signals according to their characteristics in the frequency domain. A block-based linear combination with edge detection and training set optimization is used to improve coding efficiency for complex textured areas in the EL. It is shown by experimental results the proposed lossless HIP scheme outperforms the lossless LIP scheme of H.264/AVC and JPEG-LS by a bit rate saving of 10%.; Finally, we analyze the inefficiency of the proposed lossless HIP scheme and present an enhanced hierarchical intra prediction coding called the context-based hierarchical intra prediction (CHIP). To save bits for the coding of modes, we propose a mode estimation scheme. To improve prediction accuracy, we employ the principal components analysis (PCA) to extract dominant features from the coarse representation of the base layer. The extracted features are clustered using a k-means clustering algorithm. Then, the context-based interlayer prediction (CIP) scheme is used to select the best prediction candidate without any side information. To enhance coding efficiency furthermore, an adaptive precoding process is performed by analyzing the characteristics of the prediction residual signal and a more accurate approach is proposed to estimate… Advisors/Committee Members: Kuo, C.-C. Jay (Committee Chair), Ortega, Antonio (Committee Member), Chang, Tu-Nan (Committee Member).

Subjects/Keywords: block-based intra prediction; intra prediction; linear combination prediction; lossless image compression; signal decorrelation

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APA (6th Edition):

Dai, Y. (2010). Advanced intra prediction techniques for image and video coding. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/352423/rec/543

Chicago Manual of Style (16th Edition):

Dai, Yunyang. “Advanced intra prediction techniques for image and video coding.” 2010. Doctoral Dissertation, University of Southern California. Accessed October 17, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/352423/rec/543.

MLA Handbook (7th Edition):

Dai, Yunyang. “Advanced intra prediction techniques for image and video coding.” 2010. Web. 17 Oct 2019.

Vancouver:

Dai Y. Advanced intra prediction techniques for image and video coding. [Internet] [Doctoral dissertation]. University of Southern California; 2010. [cited 2019 Oct 17]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/352423/rec/543.

Council of Science Editors:

Dai Y. Advanced intra prediction techniques for image and video coding. [Doctoral Dissertation]. University of Southern California; 2010. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/352423/rec/543

2. Harris, Jonathan. Frequency domain exploits for symmetric adaptive decorrelation.

Degree: PhD, Engineering, 2014, Massey University

Symmetric adaptive decorrelation (SAD) is a semi-blind method of separating convolutely mixed signals. While it has restrictions on the physical layout of the demixing equipment, restrictions not present for many other blind source separation (BSS) techniques, it is more ideally suited for some applications (for example, live sound mixing) due to the fact that no post-processing is required to ascertain which output corresponds with which source. Since the SAD algorithm is based on second-order statistics (SOS), it also tends to have a lower computational load when compared with those based on higher order statistics. In order to increase the e ciency of the SAD algorithm, a multibranched recursive structure is investigated. While a nominal gain in e ciency is attained, we move away from this approach in pursuit of more substantial gains. A frequency-domain symmetric adaptive decorrelation (FD-SAD) algorithm is proposed, with savings increasing not only with larger lter sizes, but also with increasing the number of sources. The convergence and stability of this new algorithm is proven. A trade-o of the FD-SAD algorithm is that it introduces a delay in the output, which renders the algorithm unsuitable for real-time applications. Therefore a hybrid approach is also proposed that does not su er from the lag of the frequency domain approach. While the proposed algorithm is slightly less computationally e cient than the pure frequency domain algorithm, it is signi cantly more e cient than the time-domain approach. A comparison of the frequency domain and hybrid algorithms shows that both achieve separation equivalent to the time-domain algorithm in a real-world environment. The proposed adaptations could also be used to extend other BSS approaches (such as Triple-N ICA for Convolutive mixtures (TRINICON) [1], which can also be based on SOS), and a comparison of the proposed methods with TRINICON is explored.

Subjects/Keywords: Symmetric adaptive decorrelation (SAD); Signal processing; Algorithms; Frequency domain

…184 D.1 Decorrelation of Multiple Non-Stationary Sources Using a Divide and Conquer… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Harris, J. (2014). Frequency domain exploits for symmetric adaptive decorrelation. (Doctoral Dissertation). Massey University. Retrieved from http://hdl.handle.net/10179/6518

Chicago Manual of Style (16th Edition):

Harris, Jonathan. “Frequency domain exploits for symmetric adaptive decorrelation.” 2014. Doctoral Dissertation, Massey University. Accessed October 17, 2019. http://hdl.handle.net/10179/6518.

MLA Handbook (7th Edition):

Harris, Jonathan. “Frequency domain exploits for symmetric adaptive decorrelation.” 2014. Web. 17 Oct 2019.

Vancouver:

Harris J. Frequency domain exploits for symmetric adaptive decorrelation. [Internet] [Doctoral dissertation]. Massey University; 2014. [cited 2019 Oct 17]. Available from: http://hdl.handle.net/10179/6518.

Council of Science Editors:

Harris J. Frequency domain exploits for symmetric adaptive decorrelation. [Doctoral Dissertation]. Massey University; 2014. Available from: http://hdl.handle.net/10179/6518

3. Krause, H.F. Correlation Structures and Prediction Models for Radar Signal Values of Sea Waves.

Degree: 2015, Universiteit Utrecht

A naval radar, though designed to detect objects or targets, also receives backscatter from wave peaks. Better understanding of the signals generated by the background waves can improve the detection of small targets. This thesis examines the correlation structure of the sea waves along the dimensions Doppler, range, azimuth and scan. The targets are mathematically simulated, as are the sample waves used alongside the measured waves. Using two prediction models – Linear Prediction and Sequential Decorrelation – examination of each cell illustrates the difference between predicted and measured signals in a plot of residual signals. High values in the resulting plot indicate target presence. Further suppressing the wave signals enhances anomalies caused by target signals via discrimination and calibration techniques. The findings matter to those wanting clearer target detection on a wave-covered sea surface situation. Advisors/Committee Members: Spitoni, C..

Subjects/Keywords: radar; signal; correlation; covariance; prediction; model; linear; sequential; decorrelation; decibel; complex; discrimination; calibration

…3.5.2 After Sequential Decorrelation… …repetition time Range Standard deviation Scan Signal strength Time Wave period Radar’s rotation… …of the sea. The radar picks up signal values for a two-dimensional area of the sea surface… …signal line segments. In Figure 1.1 (via the program in Appendix B.1) is a typical… …signal from the noise. Figure 1.2: White water peaks. Pictured in Figure 1.2 is an example… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Krause, H. F. (2015). Correlation Structures and Prediction Models for Radar Signal Values of Sea Waves. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/320788

Chicago Manual of Style (16th Edition):

Krause, H F. “Correlation Structures and Prediction Models for Radar Signal Values of Sea Waves.” 2015. Masters Thesis, Universiteit Utrecht. Accessed October 17, 2019. http://dspace.library.uu.nl:8080/handle/1874/320788.

MLA Handbook (7th Edition):

Krause, H F. “Correlation Structures and Prediction Models for Radar Signal Values of Sea Waves.” 2015. Web. 17 Oct 2019.

Vancouver:

Krause HF. Correlation Structures and Prediction Models for Radar Signal Values of Sea Waves. [Internet] [Masters thesis]. Universiteit Utrecht; 2015. [cited 2019 Oct 17]. Available from: http://dspace.library.uu.nl:8080/handle/1874/320788.

Council of Science Editors:

Krause HF. Correlation Structures and Prediction Models for Radar Signal Values of Sea Waves. [Masters Thesis]. Universiteit Utrecht; 2015. Available from: http://dspace.library.uu.nl:8080/handle/1874/320788

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