We are delighted to announce that one paper from the Future Media Art Lab (FMA Lab, Prof. Jin Wan Park) have been accepted to Tehnicki Glasnik (SCOPUS).

Title:
Optimizing Scene Transitions for Sustained Narrative Immersion in Virtual Reality Films

Authors:
Haein Yoon, Jin Wan Park

Abstract:
This paper investigates the challenges involved in adapting traditional film editing techniques for Virtual Reality (VR) films, with a particular focus on developing effective scene transitions that sustain narrative flow and enhance viewer immersion. It analyzes conventional editing methods and juxtaposes them against the unique demands of VR, leading to the proposal of solutions tailored to the immersive nature of VR. These solutions employ techniques such as the Dramatic Covenant, Long Take, and Field of View (FOV) adjustments, which are designed to improve spatial continuity and boost audience engagement in VR environments. The findings reveal that, although traditional techniques lay a fundamental groundwork, the unique characteristics of VR require a specialized approach that honors the viewer's immersive experience and their interaction within the narrative space. By developing practical strategies for filmmakers, this paper makes a contribution to the evolving field of VR films, thereby deepening our understanding of its unique narrative capabilities.

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Optics Communications (Prof. Jin Wan Park, 1 paper)

We are delighted to announce that one paper from the Future Media Art Lab (FMA Lab, Prof. Jin-Wan Park) have been accepted to Optics Communications.

Title:
Accurate mode classifi cation and image transmission via optimized deformable triple graph convolution network in fractional spatial mode optical communication

Authors:
You-na Cha, Ok-hue Cho, Jin Wan Park

Abstract:
Free-space optical (FSO) communication systems leveraging spatial-mode multiplexing offer high-capacity, low-interference data transmission, but existing methods often struggle with accurately classifying closely spaced fractional spatial modes due to limitations in feature representation, neighborhood modeling, and noise sensitivity. To overcome these challenges, this work proposes a novel Deformable Triple Attention Graph Convolutional Network with Dollmaker Optimization Algorithm (DTAGCN-DOA) for robust mode classification and high-resolution recognition of fractional Bessel-Gaussian (BG) beam-encoded data. Fractional BG beams are generated using a He–Ne laser modulated via a Spatial Light Modulator (SLM), encoding 8-bit digital symbols into structured spatial modes. A total of 51,200 beam intensity images, each corresponding to unique combinations of radial wave number and fractional orbital angular momentum, are collected and preprocessed using Masked Joint Bilateral Filtering (MJBF) to suppress noise while preserving fine spatial features. Feature extraction is conducted via a Boundary-Enhanced Patch-Merging Transformer (BEPMT), combining global semantics and boundary details. The proposed DTAGCN-DOA framework classifies fractional spatial modes using deformable graph convolutions and triple attention mechanisms, with hyperparameters optimized via the DOA. Grayscale images are encoded into 15,000 spatial modes, transmitted optically, and decoded using the trained model. It achieves 99.88 % accuracy, 31.4 dB PSNR, and 0.995 confidence, ensuring reliable, high-fidelity FSO data recognition.