This paper proposes a thorough scheme, by virtue of camera zooming descriptor with two-level threshold, to automatically retrieve close-ups directly from moving picture experts group （MPEG） compressed videos based o...This paper proposes a thorough scheme, by virtue of camera zooming descriptor with two-level threshold, to automatically retrieve close-ups directly from moving picture experts group （MPEG） compressed videos based on camera motion analysis. A new algorithm for fast camera motion estimation in compressed domain is presented. In the retrieval process, camera-motion-based semantic retrieval is built. To improve the coverage of the proposed scheme, close-up retrieval in all kinds of videos is investigated. Extensive experiments illustrate that the proposed scheme provides promising retrieval results under real-time and automatic application scenario.展开更多
为了面向低延时的浅压缩场景提供更加适配的编码方案,并降低硬件实现成本,提出一种基于数字音视频编解码技术标准(Audio Video coding Standard,AVS)浅压缩算法的帧内预测模式优化以及快速率失真优化算法。该算法通过减少原有算法帧内...为了面向低延时的浅压缩场景提供更加适配的编码方案,并降低硬件实现成本,提出一种基于数字音视频编解码技术标准(Audio Video coding Standard,AVS)浅压缩算法的帧内预测模式优化以及快速率失真优化算法。该算法通过减少原有算法帧内预测所需的预测循环次数,以及打破各块之间的数据依赖关系等措施,克服了原始方案不适合硬件流水并行处理的限制,提高了编码的效率和稳定性,从而既保障了算法的视频质量,又使新的硬件实现方案更符合实际应用需求。实验结果表明,该算法优化方案能够有效改善实际面向低延时浅压缩场景下的编码效果。展开更多
基金This work was supported by European IST FP6 Research Programme as funded for the Integrated Project:LIVE(No.IST-4-027312).
文摘This paper proposes a thorough scheme, by virtue of camera zooming descriptor with two-level threshold, to automatically retrieve close-ups directly from moving picture experts group （MPEG） compressed videos based on camera motion analysis. A new algorithm for fast camera motion estimation in compressed domain is presented. In the retrieval process, camera-motion-based semantic retrieval is built. To improve the coverage of the proposed scheme, close-up retrieval in all kinds of videos is investigated. Extensive experiments illustrate that the proposed scheme provides promising retrieval results under real-time and automatic application scenario.
文摘为了面向低延时的浅压缩场景提供更加适配的编码方案,并降低硬件实现成本,提出一种基于数字音视频编解码技术标准(Audio Video coding Standard,AVS)浅压缩算法的帧内预测模式优化以及快速率失真优化算法。该算法通过减少原有算法帧内预测所需的预测循环次数,以及打破各块之间的数据依赖关系等措施,克服了原始方案不适合硬件流水并行处理的限制,提高了编码的效率和稳定性,从而既保障了算法的视频质量,又使新的硬件实现方案更符合实际应用需求。实验结果表明,该算法优化方案能够有效改善实际面向低延时浅压缩场景下的编码效果。
