jfmcarreira
4ebe3013c0
INSTRUCTIONS Px stands for paragraph number GENERAL CORRECTIONS Fix the reference of equation "in Equation (5.2)" -> "in (5.2)" ACKNOWLEDGMENTS: Replace Dr. with Prof. where appropriate. Add reference to Loughborough University London. ABSTRACT: Remove abbreviations. Add MPEG-DASH, MOS, HM to the list. Replaced LTE with LTE-A. LIST OF ABBREVIATIONS: Remove trailing dot and add page where it is defined in the text. CHAPTER 1 Section 1.1 P1: internet -> Internet, added mention to 360-degree video and Facebook. P2: Remove sentence that was lacking reference: "However, it is known that higher coding efficiency leads to lower robustness in the presence of transmission errors and data loss." Subsequent sentence expresses the same idea as it includes reference already. P2: Add an objective score to the "poor error resilience". Section 1.2 P1: Capitalise the first letter of each objective. P1: Fix "error robust" spelling error. Section 1.3 P2: Replace "with" by "to" and efficiently with efficient. P7: Replace "material" with "materials". CHAPTER 2 Section 2.1 P1: Replace "in" with "on". Section 2.2 P1: Fix three spelling errors. P3: Replace "between" with "among" and add missing "a". P4: Add missing "a". Figure 2.1: Add description of T and Q. Section 2.3 P1: Replace "complaint" with "conforming". P2: Add missing either. Figure 2.2: Change figure to avoid confusing in arrow direction. Section 2.4 P1: Fix typo in "pictures types". Section 2.4.2 P1: Remove last part of the second sentence. P2: Replace “with” by “to”. Section 2.4.4 P1: Add missing “that”. P7: Fix two spelling errors. Section 2.5 P2: Add missing pixels. Figure 2.8: Correct the coding order Section 2.6 P1: Fix three typos. Section 2.6.1 P1: Fix third sentence to: “When the intra prediction mode is chosen for a CU, the size of the PU needs to be the same as the CU, except when the smallest CU size is selected and further division into four PUs is allowed.” P7: Fix “three-tap”. P8 (page 24): The last sentence of the paragraph was revised to: “Only the mentioned boundaries are filtered, as they are the ones most likely to introduce discontinuities.” Section 2.6.2 P1: Replace “rely” with “relies”. P2: Replace twice “in” to “of”. P2: Replace “different” with “difference”. P3: Fix two typos. P4: Add HM acronym definition. P6: Replace “not” with “no”. Table 2.2: Remove acronym in the title. Figure 2.11: Review figure and caption to become self-contained. Section 2.7 P1: remove comma after Similarly. P3: Replace “intra coded” with “intra-coded”. P4: Replace last sentence with “This procedure is the main cause for the coding distortion, as it performs a many-to-one mapping, which cannot be reversed.” Section 2.9 P1: Revise the third sentence to: “Such grid-based restriction reduces the computational complexity and facilitates the parallel-processing, which is one of the main goals of HEVC design.” P2: Revise the third sentence: “For each CTU, the encoder decides whether to apply the SAO filter or not, and if used, one of the two filter types is applied, namely the band offset or the edge offset.”
50 lines
4.8 KiB
TeX
50 lines
4.8 KiB
TeX
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\selectlanguage{english}
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\chapter*{Abstract \VERSION{0}}
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\addcontentsline{toc}{chapter}{Abstract \VERSION{3}}
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\vspace{-5mm}
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%This thesis investigates the problem of robust coding and Error Concealment (EC) in High Efficiency Video Coding (HEVC).
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%After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against transmission errors with significant impact on video quality degradation.
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%Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between Motion Vectors (MVs), by improving the decoded video quality without compromising the compression efficiency.
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%
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%The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data.
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%At the encoder stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference.
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%At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of MVs to be transmitted, as side information, to reduce mismatched MV predictions at the decoder.
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%
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%The problem of EC-aware video coding is also investigated to enhance the overall error robustness.
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%A new approach based on scalable coding and optimally EC selection is proposed, where the optimal EC modes are found by simulating transmission losses, followed by a saliency-weighted optimisation.
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%Moreover, EC residual information is encoded using a rate-controlled enhancement layer.
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%Both are transmitted to the decoder to be used in case of data loss.
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%
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%Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case.
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%A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network.
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%
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%Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software.
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%Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods.
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%\vspace{2mm}
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%\textbf{Keywords:} 3D video, frame loss error concealment, subjective quality assessment.
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This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC).
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After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against transmission errors with significant impact on video quality degradation.
|
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Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency.
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The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data.
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At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference.
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At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder.
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The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness.
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A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation.
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Moreover, recovery residual information is encoded using a rate-controlled enhancement layer.
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Both are transmitted to the decoder to be used in case of data loss.
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Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case.
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A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network.
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Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software.
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Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods.
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