基于模型的H.264编码视频翻译研究

N. Hait, D. Malah
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引用次数: 2

摘要

一种常见的视频转换方法(降低比特率)是要求变换系数。最优需求化旨在找到一组新的步长,在引入最小失真的同时达到目标比特率。由于最先进的H.264标准编码器通过限制从一个宏块到下一个宏块的量化步长变化量来限制需求化,因此不能应用常见的拉格朗日优化方法。我们通过将每个拉格朗日迭代扩展为一个有约束的动态规划阶段来解决这个依赖问题。此外,为了减少在多个步长下评估每个宏块的速率和失真的计算负荷,我们建议可以应用于此目的的分析模型。所开发的模型适合于需求化,并且与H.264中使用的上下文自适应熵编码相匹配。该算法在压缩域中进行需求化,目前只支持编码间帧。与完全穷举优化相比,它减少了4倍的运行时间,与简单的单次算法相比,它在PSNR中实现了高达1[dB]的增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Model-based Transrating of H.264 coded video
A common approach for video transrating (bit rate reduction) is to requantize the transform coefficients. Optimal requantization aims to find a set of new step-sizes that achieve the target bit rate while introducing minimal distortion. Since the state of the art H.264 standard coder constrains requantization by limiting the amount of change in the quantization step-size from one macroblock to the next, the common Lagrangian optimization approach cannot be applied. We propose a solution to this dependency problem by extending each Lagrangian iteration with a constrained dynamic programming stage. Further, in order to reduce the computational load of evaluating the rate and distortion at each macroblock for multiple step-sizes, we suggest analytic models that can be applied for this purpose. The developed models are suitable for requantization and are matched to the context-adaptive entropy coding used in H.264. The proposed algorithm performs the requantization in the compressed domain and currently supports inter coded frames only. It reduces the run-time by a factor of 4, as compared to the full exhaustive optimization, and achieves up to 1[dB] gain in PSNR, as compared to a simple one-pass algorithm.
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