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<p>This paper addresses the key challenges in film and television post-production, especially the incorporation of special effects with raw footage while maintaining consistency in lighting, shadows, and texture. With the global shift into the third era of computing, and China’s film and television industry is expanding rapidly, the research examines how digital image processing has provided more effective, interpretable, and real-time alternatives to machine learning (ML) based methods for improving the visual consistency. The proposed framework leverages advanced digital media techniques across four key stages. Motion compensation employs an Adaptive Block-Matching (ABM) algorithm with search windows, a hybrid similarity criterion, and dynamic block sizes to enhance motion estimation in multiple scenes. Image matting is supplemented with a superpixel-enhanced Grab Cut algorithm, which is blended with Bayesian refinement for effective foreground and background separation and alpha matte calculation. Non-linear filtering incorporates the generalized adaptive anisotropic diffusion equation, which utilizes the scene-adaptive parameters, cross-frame temporal gradients, and multi-scale diffusion to offer artifact-free and stable video processing. Temporal and audio synchronization is carried out at reassembly using enhanced H.264 codecs. Experimental evaluations demonstrate the efficiency of the system, attaining a high accuracy of 98.88%, Peak Signal-to-Noise Ratio (PSNR) of 39.17&#xa0;dB, Structural Similarity Index measure (SSIM) of 0.984, processing time of 0.310&#xa0;s, and motion estimation error of 0.314. This framework improves visual consistency in post-production through effective and interpretable digital processing techniques, providing a scalable and practical solution for modern film and television workflows.</p>

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Advanced techniques in digital media processing for special effects enhancement in film and television post-production

  • 期刊论文

  • 发表日期：

    2025年12月4日

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  • Wenwen Gong,
  • Qiangqiang Hou

摘要

This paper addresses the key challenges in film and television post-production, especially the incorporation of special effects with raw footage while maintaining consistency in lighting, shadows, and texture. With the global shift into the third era of computing, and China’s film and television industry is expanding rapidly, the research examines how digital image processing has provided more effective, interpretable, and real-time alternatives to machine learning (ML) based methods for improving the visual consistency. The proposed framework leverages advanced digital media techniques across four key stages. Motion compensation employs an Adaptive Block-Matching (ABM) algorithm with search windows, a hybrid similarity criterion, and dynamic block sizes to enhance motion estimation in multiple scenes. Image matting is supplemented with a superpixel-enhanced Grab Cut algorithm, which is blended with Bayesian refinement for effective foreground and background separation and alpha matte calculation. Non-linear filtering incorporates the generalized adaptive anisotropic diffusion equation, which utilizes the scene-adaptive parameters, cross-frame temporal gradients, and multi-scale diffusion to offer artifact-free and stable video processing. Temporal and audio synchronization is carried out at reassembly using enhanced H.264 codecs. Experimental evaluations demonstrate the efficiency of the system, attaining a high accuracy of 98.88%, Peak Signal-to-Noise Ratio (PSNR) of 39.17 dB, Structural Similarity Index measure (SSIM) of 0.984, processing time of 0.310 s, and motion estimation error of 0.314. This framework improves visual consistency in post-production through effective and interpretable digital processing techniques, providing a scalable and practical solution for modern film and television workflows.