A novel color compensation method for multi-view video coding (MVC) is proposed, which efficiently exploits the inter-view dependencies between views with the existence of color mismatch caused by the diversity of cam...A novel color compensation method for multi-view video coding (MVC) is proposed, which efficiently exploits the inter-view dependencies between views with the existence of color mismatch caused by the diversity of cameras. A color compensation model is developed in RGB channels and then extended to YCbCr channels for practical use. A modified inter-view reference picture is constructed based on the color compensation model, which is more similar to the coding picture than the original inter-view reference picture. Moreover, the color compensation factors can be derived in both encoder and decoder, therefore no additional data need to be transmitted to the decoder. The experimental results show that the proposed method improves the coding efficiency of MVC and maintains good subjective quality.展开更多
针对海洋牧场观测视频色彩失真和鱼类传统识别方法准确率低的问题,提出一种基于Faster-RCNN的海洋牧场鱼类识别与分类方法。首先,由于海水环境的特殊性和复杂性导致观测视频图像品质差,采用SDI(Serial Digital Interface)信号色彩补偿...针对海洋牧场观测视频色彩失真和鱼类传统识别方法准确率低的问题,提出一种基于Faster-RCNN的海洋牧场鱼类识别与分类方法。首先,由于海水环境的特殊性和复杂性导致观测视频图像品质差,采用SDI(Serial Digital Interface)信号色彩补偿系统来提高视频品质以此制作不同质量数据集;然后以Faster-RCNN为深度学习模型并提出优化特征提取网络与区域建议网络(RPN)来实现海洋牧场鱼类识别与分类。实验结果表明,该方法平均精度均值(Mean Average Precision,mAP)达到81.63%,与传统机器学习目标检测算法相比,显著提高了识别的准确率。展开更多
基金Project supported by the National Natural Science Foundation of China (No. 60772134)the Innovation Foundation of Xidian University,China (No. Chuang 05018)
文摘A novel color compensation method for multi-view video coding (MVC) is proposed, which efficiently exploits the inter-view dependencies between views with the existence of color mismatch caused by the diversity of cameras. A color compensation model is developed in RGB channels and then extended to YCbCr channels for practical use. A modified inter-view reference picture is constructed based on the color compensation model, which is more similar to the coding picture than the original inter-view reference picture. Moreover, the color compensation factors can be derived in both encoder and decoder, therefore no additional data need to be transmitted to the decoder. The experimental results show that the proposed method improves the coding efficiency of MVC and maintains good subjective quality.
文摘针对海洋牧场观测视频色彩失真和鱼类传统识别方法准确率低的问题,提出一种基于Faster-RCNN的海洋牧场鱼类识别与分类方法。首先,由于海水环境的特殊性和复杂性导致观测视频图像品质差,采用SDI(Serial Digital Interface)信号色彩补偿系统来提高视频品质以此制作不同质量数据集;然后以Faster-RCNN为深度学习模型并提出优化特征提取网络与区域建议网络(RPN)来实现海洋牧场鱼类识别与分类。实验结果表明,该方法平均精度均值(Mean Average Precision,mAP)达到81.63%,与传统机器学习目标检测算法相比,显著提高了识别的准确率。
