View-Dependent Progressive Transmission Method for 3D Building Models

Complex 3D building models, because of their huge data volume, almost always result in transmission congestion, which leads to poor user experience. To reduce the real-time transmission pressure, a novel view-dependent progressive transmission method was developed. With this method, only a small amount of transmitted data is necessary to achieve an acceptable rendering effect when the viewpoint changes. The method involves two stages. A preprocessing stage simplifies the building model using a multi-level vertex clustering algorithm. The local mesh in each clustering unit is organized into a node tree where each node includes a vertex and its related triangles. The building model is finally reorganized into a node forest. In the reconstruction stage, all root nodes are transmitted first to build a basic model. Their descendant nodes are then requested and transmitted according to viewpoint information to refine the building model during user interaction. The experimental results show that this method can effectively improve the transmission and reconstruction efficiency of 3D building models.

Figuring Out

The paper focusses on the spread of Hindu-Arabic arithmetic among European practitioners. The analysis is based on an original database recording detailed information on over 1200 practical arithmetic manuals, both manuscript and printed. This database provides the most detailed reconstruction available of the European tradition of practical arithmetic from the late 13th to the end of the 16th century. The paper argues that studying this spread makes it possible to open a perspective on a progressive transmission of ‘useful knowledge’ from the ‘commercial revolution’ to the ‘little divergence’. Focussing on the transmission of practical arithmetic allows to stress the role of skills and human capital in pre-modern European economic development. Moreover, it allows to reconstruct a progressive transmission, from the Mediterranean to the Atlantic, of a ‘practical knowledge’ which eventually contributed to major developments in European ‘theoretical knowledge’.

A Novel Hierarchical Coding Progressive Transmission Method for WMSN Wildlife Images

In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.