Lossless Encoding of Mental Cutting Test Scenarios for Efficient Development of Spatial Skills

Tóth, Róbert [Tóth, Róbert (informatikus), szerző]; Hoffmann, Miklós [Hoffmann, Miklós (matematika, infor...), szerző] Informatikai Kar (DE); Matematika Tanszék (EKKE / IK / MII); Zichar, Marianna [Zichar, Marianna (Geoinformatika / ...), szerző] Adattudomány és Vizualizáció Tanszék (DE / IK)

Angol nyelvű Szakcikk (Folyóiratcikk) Tudományos
Megjelent: EDUCATION SCIENCES 2227-7102 13 (2) Paper: 101 , 21 p. 2023
  • SJR Scopus - Computer Science (miscellaneous): Q2
  • (ÚNKP-22-3-II-DE-170)
In the last decade, various mobile applications have been developed to improve and measure spatial abilities using different spatial tests and tasks through augmented reality (AR), Virtual Reality (VR), or embedded 3D viewers. The Mental Cutting Test (MCT) is one of the most well-known and popular tests for this purpose, but it needs a vast number of tasks (scenarios) for effective practice and measurement. We have recently developed a script-aided method that automatically generates and permutes Mental Cutting Test scenarios and exports them to an appropriate file format (to GLB (glTF 2.0) assets) representing the scenarios. However, the significant number of permutations results in more than 1,000,000 assets, requiring more than 6 GB of storage space. This paper introduces an encoding scheme consisting of four stages to handle this issue through significantly reducing the storage space, making the app suitable for everyday individual use, even on a mobile phone. The proposed method encodes a subset of assets from which it can decode the whole dataset with 3% time complexity compared to classical Blender’s computations, exceeding the compression ratio of 10,000 and storage space saving 99.99%. This paper explains the features of the original assets, introduces the encoding and decoding functions with the format of documents, and then measures the solution’s efficiency based on our dataset of MCT scenarios.
Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
2023-09-25 20:56