Towards Automated Perceptual Shot Matching in Motion Pictures

Metadata

Publisher: SMPTE
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Motion Imaging Jour.
Volume: 135, No. 1, pp. 27–37
Abstract: We present an automated shot-matching algorithm that incorporates perceptual effects and scene-specific context. Focusing on viewer-centric elements, such as attention to faces or objects and overall color mood, the system uses publicly available deep learning models to detect salient features. A committee-based approach matches these aspects independently, then combines them into a single RGB balance and black-level offset per shot. Although fully automated, the system supports manual adjustments to the strictness of face, object, mood, and temporal matching. This transparent, parameterized design avoids black-box behavior and provides intuitive, editable results for colorists. Visual examples demonstrate perceptual accuracy, and a study with five professional colorists evaluates match quality and potential time savings. Comparing manual grading of raw footage with algorithm-assisted pre-matched footage, results show a human-comparable match achieved 20% faster, with the algorithm reaching about 70% of colorist quality. These findings highlight the value of extended real-world evaluation.
Publication Date: 2026-01-01
DOI: 10.5594/JMI.2026/AAZW6907
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2026/AAZW6907
Author(s): Julius Tschannerl
Daniele Siragusano
Keyword(s): Color Matching, Color Grading, Perceptual Shot Matching, Machine Learning
Copyright: © 2026 SMPTE

Source Data (JSON)

Full registry record with provenance metadata. Open directly: /api/doc/10.5594-JMI.2026-AAZW6907.json

Reference this Doc

Plain text (ISO 690 compliant)

Preview:

Julius Tschannerl and Daniele Siragusano; Towards Automated Perceptual Shot Matching in Motion Pictures, MIJ 2026, Volume 135, Number 1 (pp. 27 to 37); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/AAZW6907

Snippet:

Julius Tschannerl and Daniele Siragusano; Towards Automated Perceptual Shot Matching in Motion Pictures, MIJ 2026, Volume 135, Number 1 (pp. 27 to 37); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/AAZW6907

HTML (ISO 690 compliant)

Preview:

Snippet:

<span class="citation">Julius Tschannerl and Daniele Siragusano; <cite>Towards Automated Perceptual Shot Matching in Motion Pictures</cite>, MIJ 2026, Volume 135, Number 1 (pp. 27 to 37); SMPTE, 2026. Available at <a href="https://doi.org/10.5594/JMI.2026/AAZW6907" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2026/AAZW6907</a></span>

SMPTE's HTML Pub

Preview:

Julius Tschannerl and Daniele Siragusano; Towards Automated Perceptual Shot Matching in Motion Pictures, MIJ 2026, Volume 135, Number 1 (pp. 27 to 37); SMPTE, 2026
doi: 10.5594/JMI.2026/AAZW6907
url: https://doi.org/10.5594/JMI.2026/AAZW6907

Snippet:

<li>
Julius Tschannerl and Daniele Siragusano; <cite id="bib-10-5594-jmi-2026-aazw6907">Towards Automated Perceptual Shot Matching in Motion Pictures</cite>, MIJ 2026, Volume 135, Number 1 (pp. 27 to 37); SMPTE, 2026
<span class="doi">10.5594/JMI.2026/AAZW6907</span>
</li>