Survey details deep learning for video coding, targeting consumer electronics

This survey paper examines deep learning-based filtering (DLF) techniques for video coding, focusing on their application in consumer electronics. It addresses the challenge of computational complexity and power consumption, which hinder the deployment of DLF in devices like UHD displays and IoT gadgets. The paper categorizes DLF methods and analyzes the trade-offs between performance and hardware feasibility, highlighting the shift towards lightweight architectures suitable for Neural Processing Units (NPUs). It also considers standardization efforts in Neural Network-based Video Coding (NNVC) and identifies future challenges such as real-time inference and error propagation. AI

IMPACT Provides a roadmap for developing efficient, low-power intelligent video coding for next-generation consumer electronics.

RANK_REASON This is a survey paper on deep learning techniques for video coding, not a new model release or significant industry event. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

• 8K resolution
• Consumer Electronics
• Deep Learning-based Filtering
• HEVC/H.265
• High Efficiency Video Coding
• Internet of Things
• Joint Video Experts Team
• Neural Network-based Video Coding
• Neural Processing Units
• NNVC
• Versatile Video Coding
• Video coding
• VVC/H.266

• paper
• infra

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