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Quantization Parameter Control and Fast Code Modification for Fast Stream Joiner
DOI:10.1109/TCE.2012.6415010.png)
摘要
En 中文
Multi-vision systems and panoramic video systems are expected to produce a new multimedia paradigm. However, their increased processing load on transmission servers handling large amounts of video and high-definition video must be considered. Our previously presented multi-vision system enables many users to view multiple videos by virtue of fast stream joining algorithms. The system joins (combines) much video stream on a transmission server into a single video stream. Then it transmits the joined single video stream to various client devices including mobile terminals. Because of its single-stream transmission, the client device enables decoding of the received video stream without special mechanisms. Especially, to join multiple video streams with high performance and without prediction inconsistency, the encoder fixes the number of a non-zero quantized coefficient (called TotalCoeff) to a certain in earlier work. Because this scheme of the fixing TotalCoeff in the encoder phase obviates the decoding process of each stream and modification of the prediction relation in the joining phase, it is possible to implement a stream joiner with a lightweight process. However, image degradation occurs because this scheme adds and drops the quantized coefficient. We emphasize that the amount of image degradation (called TotalCoeff fixing error or simply error) is dependent on the original TotalCoeff before fixing TotalCoeff and the Quantization Parameter (QP) because the quantization process has zero-rounding capability. Then we consider, using QP, the TotalCoeff estimation model and the error estimation model created by adding and deleting the coefficient. Under these models, we propose a QP control scheme to decrease this degradation without sacrificing high speed in the joiner(1).
Keyword:
Tile video stream
tile stream joiner
H.264/AVC
error model
coefficient estimation
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期刊
IF:
10.9
论文数:
5.3K
被引数:
6.8K

