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An ROI-codec-supported rate control algorithm in video compression

机译:视频压缩中的ROI编解码率控制算法

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For low bit rate video compression, the quality of reconstructed video is usually poor. The high codec priority of region of interest (ROI) can improve image quality obviously. Nowadays, video segmentation methods are often used for extracting ROI, but these methods have high computational complexity and are not satisfied to real time communication. On the other hand, in most existing rate control algorithms, ROI can't select the low and high bit rate R-Q model adaptively. Aiming at these problems, in this paper, a simple and efficient approach of extracting ROI is proposed which can decrease the computational complexity of existing ROI extracting algorithms. Bits are distributed to ROI and non-ROI (NROI) respectively according to the image complexity and motion information. Moreover, the judgment criterion of distinguishing between low and high bit rate coding category is derived, which makes the algorithm select the R-Q model adaptively and decrease the rate control errors. In addition, the scheme of modifying the coding order of macro blocks (MBs) can enhance the objective image quality. Experiment results demonstrate that the proposed algorithm achieves a bit rate closer to the target, provides fewer skipped frames, and gets better objective and subjective image quality significantly compared with TMN7 and TMN8 algorithms.
机译:对于低比特率视频压缩,重建视频的质量通常很差。感兴趣区域(ROI)的高编解码器优先级可以显然可以提高图像质量。如今,视频分段方法通常用于提取ROI,但这些方法具有高计算复杂性,并且对实时通信不满意。另一方面,在大多数现有速率控制算法中,ROI无法自适应地选择低比特率R-Q模型。针对这些问题,在本文中,提出了提取ROI的简单有效的方法,这可以降低现有ROI提取算法的计算复杂性。比特分别根据图像复杂性和运动信息分发给ROI和非ROI(NROI)。此外,导出了区分低比特率编码类别的判断标准,这使得算法自适应地选择R-Q模型并降低速率控制误差。另外,修改宏块的编码顺序(MBS)的方案可以增强目标图像质量。实验结果表明,所提出的算法实现了更接近目标的比特率,提供更少的跳过帧,并与TMN7和TMN8算法相比,更好地获得更好的客观和主观图像质量。

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