We presen t LBW-Net,an efficient optimization based method for qua nt ization and training of the low bit-width convolutional neural networks(CNNs).Specifically,we quantize the weights to zero or powers of 2 by minimi...We presen t LBW-Net,an efficient optimization based method for qua nt ization and training of the low bit-width convolutional neural networks(CNNs).Specifically,we quantize the weights to zero or powers of 2 by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation(weight learning).We characterize the combinatorial nature of the low bit-width quantization problem.For 2-bit(ternary)CNNs,the quantization of N weights can be done by an exact formula in O(N log N)complexity.When the bit-width is 3 and above,we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally inexpensive.The free parameter is further determined by network retraining and object detection tests.The LBW-Net has several desirable advantages over full-precision CNNs,including considerable memory savings,energy efficiency,and faster deployment.Our experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart,the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks,and can even do better in real world visual scenes,while empirically enjoying more than 4× faster deployment.展开更多
由于水下声波信道带宽窄,难以采用高效视频编码(High Efficiency Video Coding,HEVC)实现水下视频低码率传输。提出了一种基于对象的水下视频低码率编码算法。首先对水下对象视频进行时空域下采样以降低其数据量,再采用低延时模式编码...由于水下声波信道带宽窄,难以采用高效视频编码(High Efficiency Video Coding,HEVC)实现水下视频低码率传输。提出了一种基于对象的水下视频低码率编码算法。首先对水下对象视频进行时空域下采样以降低其数据量,再采用低延时模式编码少量视频帧作为参考帧。然后,提取水下对象视频非参考帧的特征点,并对特征点和对象掩膜进行编码。在解码端用特征点和掩膜进行对象的粗糙重建,获得对象的初步轮廓和颜色信息。最后,根据粗糙重建对象和参考帧对象的映射关系,采用基于在线学习的方法实现对象的精细重建。实验结果表明,与HEVC相比,所提算法的BDBR-SSIM(Bjontegarrd Delta Bit Rate and Structural Similarity)降低了14.88%。展开更多
In the context of object oriented video coding, the encoding of segmentation maps defined by contour networks is particularly critical. In this paper, we present a lossy contour network encoding algorithm where both t...In the context of object oriented video coding, the encoding of segmentation maps defined by contour networks is particularly critical. In this paper, we present a lossy contour network encoding algorithm where both the rate distortion contour encoding based on maximum operator and the prediction error for the current frame based on quadratic motion model are combined into a optimal polygon contour network compression scheme. The bit rate for the contour network can be further reduced by about 20% in comparison with that in the optimal polygonal boundary encoding scheme using maximum operator in the rate distortion sense.展开更多
文摘We presen t LBW-Net,an efficient optimization based method for qua nt ization and training of the low bit-width convolutional neural networks(CNNs).Specifically,we quantize the weights to zero or powers of 2 by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation(weight learning).We characterize the combinatorial nature of the low bit-width quantization problem.For 2-bit(ternary)CNNs,the quantization of N weights can be done by an exact formula in O(N log N)complexity.When the bit-width is 3 and above,we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally inexpensive.The free parameter is further determined by network retraining and object detection tests.The LBW-Net has several desirable advantages over full-precision CNNs,including considerable memory savings,energy efficiency,and faster deployment.Our experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart,the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks,and can even do better in real world visual scenes,while empirically enjoying more than 4× faster deployment.
文摘由于水下声波信道带宽窄,难以采用高效视频编码(High Efficiency Video Coding,HEVC)实现水下视频低码率传输。提出了一种基于对象的水下视频低码率编码算法。首先对水下对象视频进行时空域下采样以降低其数据量,再采用低延时模式编码少量视频帧作为参考帧。然后,提取水下对象视频非参考帧的特征点,并对特征点和对象掩膜进行编码。在解码端用特征点和掩膜进行对象的粗糙重建,获得对象的初步轮廓和颜色信息。最后,根据粗糙重建对象和参考帧对象的映射关系,采用基于在线学习的方法实现对象的精细重建。实验结果表明,与HEVC相比,所提算法的BDBR-SSIM(Bjontegarrd Delta Bit Rate and Structural Similarity)降低了14.88%。
基金upported by the National Natural Science Foundation of China!( 6 95 72 0 2 3)bytheKeyProjectfromtheShanghaiEducationComm
文摘In the context of object oriented video coding, the encoding of segmentation maps defined by contour networks is particularly critical. In this paper, we present a lossy contour network encoding algorithm where both the rate distortion contour encoding based on maximum operator and the prediction error for the current frame based on quadratic motion model are combined into a optimal polygon contour network compression scheme. The bit rate for the contour network can be further reduced by about 20% in comparison with that in the optimal polygonal boundary encoding scheme using maximum operator in the rate distortion sense.
