Jigsaw puzzle difficulty assessment and analysis of influencing factors based on deep learning method

• Published in: The Visual computer 2024-10, Vol.40 (10), p.7133-7145
• Main Authors: Yuan, Yuetao, Xu, Shuchang, Lin, Shudong
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Cognitive ability; Computer Graphics; Computer Science; Datasets; Deep learning; Design; Designers; Games; Image Processing and Computer Vision; Jigsaw puzzles; Machine learning; Neural networks; Puzzles; Skills; Texture; User experience
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-024-03387-2

Jigsaw puzzle is a casual game that can be used for leisure and stress relief. This paper presents a novel algorithm for quantifying and estimating the time required for users to complete jigsaw puzzle games and providing game difficulty reference for game designers. Firstly, a difficulty quantification model is proposed. Then, based on observation and hypothesis, it is believed that jigsaw puzzle difficulty is related to elements such as texture in the puzzle. Finally, experimental validation demonstrates that jigsaw puzzle difficulty is related to the texture and number of repeated elements in the puzzle. The algorithm is tested on a large amount of jigsaw puzzle game datasets, subsequently verifying its effectiveness and accuracy. The main contribution of this algorithm is to provide a new quantitative evaluation method for jigsaw puzzle game difficulty, which can assist game designers in optimizing game difficulty and enhancing user experience. Our data, code, and model are available at CunHua-YYT/JigsawSort (github.com).