Virtual reality, free-viewpoint television, virtual navigation, 360 degrees video are the areas of research and technology that need efficient compression of multiview video plus depth acquired by cameras with arbitra...
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ISBN:
(纸本)9781728147048
Virtual reality, free-viewpoint television, virtual navigation, 360 degrees video are the areas of research and technology that need efficient compression of multiview video plus depth acquired by cameras with arbitrary positions. Astonishingly, proliferation of 3D extensions of AVC and HEVC technology is very low. Therefore, in this paper, we present a study on independent coding of views and depth maps. A simple technique is proposed to estimate quantization step for depth as a function of the quantization step for multiview video. This technique is studied in the context of multiview video plus depth acquired using cameras located around a scene. The approach is based on simple modeling of the relation between quantization parameters for depth and multiview video. The experimental results are obtained for stereoscopic video with two respective depth maps. For standard MPEG test sequences, the results demonstrate usefulness of the approach for HEVC, VVC, MV-HEVC codecs.
Training a machine learning model with federated edge learning(FEEL)is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge *** this study,the tr...
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Training a machine learning model with federated edge learning(FEEL)is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge *** this study,the training time minimization problem is investigated in a quantized FEEL system,where heterogeneous edge devices send quantized gradients to the edge server via orthogonal *** particular,a stochastic quantization scheme is adopted for compression of uploaded gradients,which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication *** training time is modeled by taking into account the communication time,computation time,and the number of communication *** on the proposed training time model,the intrinsic trade-off between the number of communication rounds and per-round latency is ***,we analyze the convergence behavior of the quantized FEEL in terms of the optimality ***,a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap,based on which the closed-form expressions for the number of communication rounds and the total training time are *** by the total bandwidth,the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization *** this end,an algorithm based on alternating optimization is proposed,which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection *** different learning tasks and models,the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.
Cassava is the third largest source of carbohydrates for human consumption worldwide;however, it is highly susceptible to viral and bacterial diseases, which pose a significant threat to food security. The advancement...
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Cassava is the third largest source of carbohydrates for human consumption worldwide;however, it is highly susceptible to viral and bacterial diseases, which pose a significant threat to food security. The advancement of deep learning algorithms in precision agriculture holds the key to enabling the early classification of plant diseases, thereby leading to enhanced crop yields and ultimately stabilizing food security. In the coarse-grained label discrimination task of weak supervision learning, high-quality semantic features contain abundant semantic description information, which plays a crucial role in constructing a precise description of plant disease discrimination in tanglesome field circumstances and directly influences the performance of neural networks. Thus, a multiattention IBN anti-aliasing neural network (MAIANet) was proposed to improve the classification accuracy of cassava leaf disease classification by improving the feature quality in the coarseness label classification task. The proposed MAIANet neural network includes two innovative approaches. First, the multiattention method was designed to scale the feature signals twice to adjust the angular frequency of the feature signals in the residual branch for optimal feature fitting within the residual unit. Second, the anti-aliasing block extracts the high-frequency component feature and optimizes the quantization result of the pooling operation to depress the aliasing signal in the down-sampled feature maps. When the proposed method was tested and validated on the cassava dataset, the results showed that the prediction accuracy of the proposed method significantly improved, with an accuracy of 95.83 %, a loss of 1.720, and an F1-score of 0.9585, outperforming V2-ResNet101, EfficientNet-B5, RepVGG-B3g4, and AlexNet with significant margins. Based on the above experimental results, the proposed algorithm is suitable for classifying cassava leaf diseases.
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