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A novel adaptive quantization method for video coding
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AI
DOI：10.1007/s11042-017-5064-4.png)
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Abstract
En 中文
In this paper, we propose a novel AQ (Adaptive Quantization) algorithm to improve the subjective coding performance. Firstly, the factors affecting a suitable adaptive quantization method are carefully analyzed. Two important conclusions are drawn to guide designing a suitable AQ method. Secondly, based on the drawn analysis, a novel SATD (Sum of the Transformed Difference) based temporal adaptive quantization method is proposed. The proposed method fully considers the temporal characteristics, which can produce more visual-friendly QP (quantization parameter) offset distribution. Experiments are performed on x264 and HM16.0, respectively. With SSIM (Structure Similarity) as the metric, more than 21.07% BD-Rate (Bjontegaard-Delta Rate) saving can be achieved on x264, and on HM16.0, 8.07% and 7.99% BD-Rate savings can be obtained for LDP (Low-Delay-main-P) and LDB (Low-Delay-main-B) configurations, respectively, which is better than the state-of-the-art methods.
Keywords:
Inter-frame dependency
Adaptive quantization
QP offset
SSIM
Subjective quality
Journal
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3
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1.9W
Citations:
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