.png){kind=icon}
.png){kind=icon}
Return
Quantizer offset selection for improved requantization transcoding
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2011.02.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we provide an analysis of the requantization problem in order to improve the requantization process. This analysis is based on theoretical R-D results of requantized Laplacian sources instead of minimizing requantization errors as commonly found in the literature. We derive the effective quantizer characteristic by applying superposition to the quantizer characteristics of encoder and transcoder. Further investigation shows that the effective quantizer has a periodic property. Using the memoryless property of the probability distribution function and the periodic property of the effective quantizer characteristic, we derive expressions for entropy and distortion. Based on the theoretical R-D model, requantization for fine and coarse quantized signals is investigated. The analysis of the R-D behavior shows that a heuristic can be derived which improves the requantization process. Finally, the results from the R-D analysis are verified for requantization transcoding of H.264/AVC video streams. We show that the transcoding process for H.264/AVC video streams, which corresponds to coarse quantization, is improved with gains up to 1 dB. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Video processing
Transcoding
Requantization
Quantizer offset
H.264/AVC
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
S
IF:
2.7
Papers:
2.8K
Citations:
4.2K