A rate-control scheme using fast frame-rate controlling and adaptive rate-quantization modeling for h.263

Din-Yuan Chan, Chun-Yuan Chang, Shou-Jen Lin, Chung-Wei Lin
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引用次数: 1

Abstract

Bit-rate control plays a critical role in thc video-coding. One of the main challenges of video-coding is to provide the guaranteed quality under highly compressed video. In our study, a structured R-Q base rate-control framework is proposed for low-delay video coding, which contains thrcc processing stages: the control of encoding frame-rate, bit-allocation for frame-level and the decision of quantization parameters for macroblock-level. First, thc proposed framework decides the encoding frame-rate per group of pictures (COPS) for better motion continuity. In the second stage, target-bit per frame is effectively estimated with the frame texture and buffer fullness. Last, based on an adaptive R-Q model modified by the Kalman filter for each cluster of MBs (COBs), a suitable.quantization parameter of each MB can be confirmed. In comparison with the TMNXrc module, our new framework can achieve higher objective and perceptual quality with lower computational complexity'. Index Terms H.263, Rate-Quantization Model, Kalman filter.
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一种基于快速帧率控制和自适应速率量化建模的h.263帧率控制方案
码率控制在视频编码中起着至关重要的作用。视频编码的主要挑战之一是在高度压缩的情况下保证视频的质量。本文提出了一种用于低延迟视频编码的结构化R-Q基率控制框架,该框架包含三个处理阶段:编码帧率控制、帧级比特分配和宏块级量化参数的确定。首先,该框架决定了每组图像的编码帧率(cop),以获得更好的运动连续性。在第二阶段,利用帧纹理和缓冲区满度有效估计每帧的目标比特数。最后,基于卡尔曼滤波修正的自适应R-Q模型,对每一簇mb (cob)进行了合适的匹配。可以确定每个MB的量化参数。与TMNXrc模块相比,我们的框架能够以更低的计算复杂度实现更高的客观和感知质量。索引条款H.263,率量化模型,卡尔曼滤波。
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