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.
