3D modellerin dayanıklı iletimi.

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2010

Tezin Dili: İngilizce

Öğrenci: Mehmet Oğuz Bici

Danışman: GÖZDE AKAR

Özet:

In this thesis, robust transmission of 3D models represented by static or time consistent animated meshes is studied from the aspects of scalable coding, multiple description coding (MDC) and error resilient coding. First, three methods for MDC of static meshes are proposed which are based on multiple description scalar quantization, partitioning wavelet trees and optimal protection of scalable bitstream by forward error correction (FEC) respectively. For each method, optimizations and tools to decrease complexity are presented. The FEC based MDC method is also extended as a method for packet loss resilient transmission followed by in-depth analysis of performance comparison with state of the art techniques, which pointed significant improvement. Next, three methods for MDC of animated meshes are proposed which are based on layer duplication and partitioning of the set of vertices of a scalable coded animated mesh by spatial or temporal subsampling where each set is encoded separately to generate independently decodable bitstreams. The proposed MDC methods can achieve varying redundancy allocations by including a number of encoded spatial or temporal layers from the other description. The algorithms are evaluated with redundancy-rate-distortion curves and per-frame reconstruction analysis. Then for layered predictive compression of animated meshes, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial/temporal prediction and angular relations of triangles between current and previous frames. The experimental results show that compared to state of the art scalable predictive coder, up to 30\% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level. Finally, optimal quality scalability support is proposed for the state of the art scalable predictive animated mesh coding structure, which only supports resolution scalability. Two methods based on arranging the bitplane order with respect to encoding or decoding order are proposed together with a novel trellis based optimization framework. Possible simplifications are provided to achieve tradeoff between compression performance and complexity. Experimental results show that the optimization framework achieves quality scalability with significantly better compression performance than state of the art without optimization.