compression algorithm
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2021 ◽  
pp. 149-178
Author(s):  
Jordan Schonig

This chapter examines the aesthetic properties and phenomenological effects of compression glitches—blocky image distortions that momentarily deform digitally compressed video. As visible expressions of the invisible processes of digital video compression, compression glitches offer unprecedented encounters with the technological production of cinematic motion. Two distinct consequences of these encounters are explored in this chapter. First, because compression glitches are more likely to occur when the compression algorithm is overworked by large volumes of onscreen movement, the ubiquity of compression glitches has yielded a spectatorial sensitivity to the magnitude of movement on screen. Second, because compression glitches extract movement itself (i.e., algorithmic motion instructions) from its original visual context, the visual qualities of such glitches heighten our attention to the formal qualities of movement as distinct from the actions and events that such movements comprise. Taken together, these two spectatorial effects of the compression glitch illuminate new orientations toward cinematic motion in the digital era. Describing these orientations, the chapter argues, can model a form of inquiry that bridges the gap between technologically oriented and phenomenologically oriented accounts of “digital cinema.”


Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8275
Author(s):  
Gang Liu ◽  
Lei Jia ◽  
Taishan Hu ◽  
Fangming Deng ◽  
Zheng Chen ◽  
...  

For the problem of data accumulation caused by massive sensor data in transmission line condition monitoring system, this paper analyzes the type and amount of data in the transmission line sensor network, compares the compression algorithms of wireless sensor network data at home and abroad, and proposes an efficient lossless compression algorithm suitable for sensor data in transmission line linear heterogeneous networks. The algorithm combines the wavelet compression algorithm and the neighborhood index sequence algorithm. It displays a fast operation speed and requires a small amount of calculation. It is suitable for battery powered wireless sensor network nodes. By combining wavelet correlation analysis and neighborhood index sequence coding, the compression algorithm proposed in this paper can achieve a high compression rate, has strong robustness to packet loss, has high compression performance, and can help to reduce network load and the packet loss rate. Simulation results show that the proposed method achieves a high compression rate in the compression of the transmission line parameter dataset, is superior to the existing data compression algorithms, and is suitable for the compression and transmission of transmission line condition monitoring data.


2021 ◽  
Author(s):  
Chen-Hao Hsu ◽  
Wan-Hsuan Lin ◽  
Wei-Hsiang Tseng ◽  
Yao-Wen Chang

2021 ◽  
Vol 10 (12) ◽  
pp. 817
Author(s):  
Zhihong Ouyang ◽  
Lei Xue ◽  
Feng Ding ◽  
Da Li

Linear approximate segmentation and data compression of moving target spatio-temporal trajectory can reduce data storage pressure and improve the efficiency of target motion pattern mining. High quality segmentation and compression need to accurately select and store as few points as possible that can reflect the characteristics of the original trajectory, while the existing methods still have room for improvement in segmentation accuracy, reduction of compression rate and simplification of algorithm parameter setting. A trajectory segmentation and compression algorithm based on particle swarm optimization is proposed. First, the trajectory segmentation problem is transformed into a global intelligent optimization problem of segmented feature points, which makes the selection of segmented points more accurate; then, a particle update strategy combining neighborhood adjustment and random jump is established to improve the efficiency of segmentation and compression. Through experiments on a real data set and a maneuvering target simulation trajectory set, the results show that compared with the existing typical methods, this method has advantages in segmentation accuracy and compression rate.


2021 ◽  
Author(s):  
Jingchuan Wang ◽  
Jin Li ◽  
Hua Jin ◽  
Xiang Chen

2021 ◽  
Author(s):  
Svetozar Milykh ◽  
Sergey Kovalchuk

Learning treatment methods and disease progression is significant part of medicine. Graph representation of data provides wide area for visualization and optimization of structure. Present work is dedicated to suggest method of data processing for increasing information interpretability. Graph compression algorithm based on maximum clique search is applied to data set with acute coronary syndrome treatment trajectories. Results of compression are studied using graph entropy measures.


2021 ◽  
Author(s):  
Shumei Liu ◽  
Hongmei Yang ◽  
Jeng-Shyang Pan ◽  
Tao Liu ◽  
Bin Yan ◽  
...  

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