A run length encoding based variable byte compression of binary layer projection images for MSLA and DLP 3D printers

Date

2024-9-03

Author

Arslan, Semih

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This study addresses the development of a computationally efficient lossless compression algorithm utilizing run-length encoding (RLE) on single binary images or group of consecutive binary images. The images are the layer projection images which are generated by the slicing process of masked stereolithography (mSLA) and digital light processing (DLP) 3D printers. The variable byte encoding is used to store run length integer counts with higher compression ratios. The primary aim of this algorithm is to enable easy execution on single board computers while achieving higher compression levels compared to the conventional image compression methods. Additionally, various space filling curves are employed and compared for converting single images or group of images into 1D arrays to be used by run length encoding algorithm. Performance of methods such as line, perimeter, zigzag, and boustrophedonic scan are evaluated to determine their effectiveness in data compression process. Moreover, to use the similarity between consecutive images more effectively, bitwise XOR operation was employed. For consecutive images with high similarity, bitwise XOR operation removes the redundancy in the data representation and allows RLE algorithm to compress data more efficiently. The developed algorithm's performance was assessed by comparing it with current compression methods. The results show that the suggested algorithm can reach higher compression ratios for binary images and have effective resource management, making it suitable for use on single board computers. This effectiveness guarantees the best use of resources, especially in environments with limited resources, thereby improving its efficiency in real-world scenarios.

Subject Keywords

Binary image compression, Variable byte encoding, Volumetric run length encoding, Space filling curves, Masked stereolithography

URI

https://hdl.handle.net/11511/111250

Collections

Graduate School of Natural and Applied Sciences, Thesis