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

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Abstract

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).

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Funding

This study was funded by Beijing Dailybread CO., LTD and the School of Information Science and Technology, Hangzhou Normal University.

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Authors and Affiliations

  1. School of Information Science and Technology, Hangzhou Normal University, Hangzhou, 311121, China

    Yuetao Yuan & Shuchang Xu

  2. Beijing Dailybread Co., Ltd., Beijing, 100089, China

    Shudong Lin

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  1. Yuetao Yuan
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  2. Shuchang Xu
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  3. Shudong Lin
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Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by SL, SX, and YY. The first draft of the manuscript was written by YY and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Shuchang Xu.

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Contributing authors: 2022112011003@stu.hznu.edu.cn

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Yuan, Y., Xu, S. & Lin, S. Jigsaw puzzle difficulty assessment and analysis of influencing factors based on deep learning method. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03387-2

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