Sensor Data Compression

Dear Colleagues,

Data compression is one of the most traditional disciplines in computer science with a huge amount of engineering applications. In fact, except textual data and various synthetic datasets, either entered interactively or generated algorithmically, the majority of the remaining data are produced by some kinds of sensors. For example, a raster image is obtained by an image sensor, and digital sound is acquired by a sound sensor. These types of data are so common today that they are not considered to result from sensors anymore. Indeed, today, sensor data are considered to be produced by various types of remote sensing or IoT devices. The total amount of data may be challenging as the sampling frequency of sensors can be very high and because huge amounts of sensors may operate in parallel. These data should be sent to the receiver for further processing, analysing, and archiving. To reduce network traffic, speed up the transmission, save energy and memory space, and enable the operation in the real-time, these data should be compressed.

Many challenges still exist for the compression of sensor data. This Special Issue searches for the innovative papers that deal with various aspects of data compression obtained from various sensors as follows:

• Background of data compression (e.g., surveys, methodological concepts);
• Image compression (e.g., medical and satellite images, aerial photographs, orthophoto);
• Multichannel, aggregated, and time-varying data compression (e.g. IoT, biomedical signals, and sound data);
• Point cloud compression (e.g., LiDAR or photogrammetry data);
• Voxel data compression (e.g., CT, MRI, ultrasound)
• Data compression in the ad hoc wireless sensor networks (e.g., energy saving).

Prof. Dr. Borut Žalik
Dr. Damjan Strnad
Guest Editors

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• sensors
• data compression
• algorithms
• remote sensing
• IoT
• earth observation
• biomedical data