Video coding standards such as H.264 AVC (Advanced Video Coding) rely on predictive coding to achieve high compression efficiency. Predictive coding consists of predicting each frame using preceding frames. However, predictive coding incurs a cost when transmitting over unreliable networks: frames are no longer independent and the loss of data in one frame may affect future frames. In this thesis, we study the effectiveness of Flexible Macroblock Ordering (FMO) in mitigating the effect of errors on the decoded video and propose solutions to improve the error concealment on H.264 decoders.
After introducing the subject matter, we present the H.264 profiles and briefly determine their intended applications. Then we describe FMO and justify its usefulness for transmission over lossy networks. More precisely, we study the cost in terms of overheads and the improvements it offers in visual quality for damaged video frames. The unavailability of FMO in most H.264 profiles leads us to design a lossless FMO removal scheme, which allows the playback of FMO-encoded video on non FMO-compliant decoders. Then, we describe the process of removing the FMO structure but also underline some limitations that prevent the application of the scheme. Finally, we assess the induced overheads and propose a model to predict these overheads when FMO Type 1 is employed.
Eventually, we develop a new error concealment method to enhance video quality without relying on channel feedback. This method is shown to be superior to existing methods, including those from the JM reference software and can be applied to compensate for the limitations of the scheme proposed FMO-removal scheme. After introducing our new method, we evaluate its performance and compare it to some classical algorithms.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/34715 |
| Date | 08 July 2009 |
| Creators | Mazataud, Camille |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
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