Original scientific paper
https://doi.org/10.32985/ijeces.16.4.4
Enhancing Energy Efficiency in GAN-based HEVC Video Compression Using Knowledge Distillation
Hajar Hardi
; Sciences and Techniques for the Engineer Laboratory National School of Applied Sciences University Sultan Moulay Slimane Khouribga, Morocco
*
Imade Fahd Eddine Fatani
; Sciences and Techniques for the Engineer Laboratory National School of Applied Sciences University Sultan Moulay Slimane Khouribga, Morocco
* Corresponding author.
Abstract
High-efficiency Video Coding (HEVC) is a widely used video coding standard, and it has recently gained widespread adoption in various applications, such as video streaming, broadcasting, real-time conferencing, and storage. The adoption of Generative Adversarial Networks (GANs) into HEVC compression has shown significant improvements in compression performance by reducing the video size while maintaining the original quality. In this work, we explore the application of Knowledge Distillation to reduce the energy consumption associated with GAN-based HEVC. By training a smaller student model that imitates the larger teacher model's behavior, we significantly improved energy efficiency. In this paper, we provide a detailed study comparing the traditional HEVC algorithm, GAN-based HEVC, and GAN-based HEVC with Knowledge Distillation. The experimental results demonstrate a reduction in energy consumption of up to 30% while preserving video quality, making it an effective solution for video streaming platforms and energy-constrained devices and a sustainable solution for video compression without diminishing video quality.
Keywords
HEVC; HIGH EFFICIENCY VIDEO CODING; VIDEO COMPRESSION; GAN; GENERATIVE ADVERSARIAL NETWORKS; KNOWLEDGE DISTILLATION; STUDENT-TEACHER MODEL; POWER CONSUMPTION OPTIMIZATION; ENERGY EFFICIENCY;
Hrčak ID:
329961
URI
Publication date:
7.4.2025.
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