Article: Deep learning-based semantic segmentation for colour language analysis: a computational framework for cinematic media Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.95 pp.89 - 108 Abstract: Against the backdrop of the rapid development of the global film industry and digital media technology (DGMT), colour aesthetics has evolved into a core medium for film narrative and cross-cultural communication. Addressing the shortcomings of traditional film colour research, which relies on manual annotation and is susceptible to subjective bias, this study proposes an improved semantic segmentation framework (Gabor-HR-DeepLab) that integrates dual-stream weighted Gabor features and high-resolution neural networks. Through refined pixel segmentation and dynamic colour palette extraction of over 20,000 frames from the Eastern film series Ne Zha and the Western Marvel Cinematic Universe (MCU), the differences between the two in dimensions such as colour contrast, brightness variance, and harmony are quantitatively analysed. This research result profoundly reveals the differences between the localised cultural aesthetics representation and the standardised industrial visual strategies. Crucially, this study has made a significant contribution to information and communication technology (ICT) by introducing a robust automated digital media processing procedure; it provides quantifiable empirical evidence by combining computational algorithms with humanistic film analysis; and it offers an expandable computational framework for the digital animation industry. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning-based semantic segmentation for colour language analysis: a computational framework for cinematic media

Authors: Jianan Zhou

Addresses: School of Art and Design, Shanghai Institute of Technology, Shanghai, 200235, China

Abstract: Against the backdrop of the rapid development of the global film industry and digital media technology (DGMT), colour aesthetics has evolved into a core medium for film narrative and cross-cultural communication. Addressing the shortcomings of traditional film colour research, which relies on manual annotation and is susceptible to subjective bias, this study proposes an improved semantic segmentation framework (Gabor-HR-DeepLab) that integrates dual-stream weighted Gabor features and high-resolution neural networks. Through refined pixel segmentation and dynamic colour palette extraction of over 20,000 frames from the Eastern film series Ne Zha and the Western Marvel Cinematic Universe (MCU), the differences between the two in dimensions such as colour contrast, brightness variance, and harmony are quantitatively analysed. This research result profoundly reveals the differences between the localised cultural aesthetics representation and the standardised industrial visual strategies. Crucially, this study has made a significant contribution to information and communication technology (ICT) by introducing a robust automated digital media processing procedure; it provides quantifiable empirical evidence by combining computational algorithms with humanistic film analysis; and it offers an expandable computational framework for the digital animation industry.

Keywords: deep learning; DL; semantic segmentation; information and communication technology; ICT; multimedia processing; colour language computing; cross-cultural film aesthetics; computer vision; CV.

DOI: 10.1504/IJICT.2026.156133

International Journal of Information and Communication Technology, 2026 Vol.27 No.95, pp.89 - 108

Received: 30 Apr 2026
Accepted: 25 May 2026
Published online: 04 Sep 2026 *

Title: Deep learning-based semantic segmentation for colour language analysis: a computational framework for cinematic media

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