0% Complete
Home
/
15th International Conference on Computer and Knowledge Engineering
VVC-AAR: Adaptive Attention-Aware Resolution and Residual Coding for Perceptually Optimized Ultra-Low Bitrate VVC Compression
Authors :
Yaghoub Saberi
1
Somayeh Arab Najafabadi
2
Mohammadreza Hemmati
3
1- Department of Computer Engineering Na.C. , Islamic Azad University, Najafabad, Iran
2- Department of Computer Engineering Na.C. , Islamic Azad University, Najafabad, Iran
3- Department of Computer Engineering Na.C. , Islamic Azad University, Najafabad, Iran
Keywords :
VVC،Video Compression،Attention Map،Neural Residual Coding،Perceptual Quality،Adaptive Resolution،Low Bitrate Streaming
Abstract :
this paper presents VVC-AAR, a saliency-guided resolution adaptation framework designed to enhance the coding efficiency of the Versatile Video Coding (VVC) standard. The proposed method employs an importance map derived from visual salience cues to classify video regions into multiple perceptual importance levels. High-importance regions are encoded at full resolution with low quantization parameters. Low-importance areas are down-sampled and compressed more aggressively. They are later refined through neural-based enhancement to restore visual quality. This adaptive strategy enables the encoder to achieve more effective rate allocation according to perceptual relevance. Experimental results on standard test sequences and high-resolution datasets demonstrate that VVC-AAR achieves significant bitrate savings up to 35% over the VVC anchor (VTM 12.0) at comparable or improved perceptual quality, as measured by VMAF. These findings confirm that perception-driven compression with resolution adaptation can provide substantial coding gains without compromising subjective visual experience.
Papers List
List of archived papers
A routing method with the approach of reducing energy consumption in WSNs with the Jellyfish Search (JS) optimizer algorithm and unequal clustering
Ehsan Gholami - Javad Hamidzadeh
SASIAF, An Scalable Accelerator For Seismic Imaging on Amazon AWS FPGAs
Mostafa Koraei - S.Omid Fatemi
A Weighted TF-IDF-based Approach for Authorship Attribution
Ali Abedzadeh - Reza Ramezani - Afsaneh Fatemi
Android Malware Detection using Supervised Deep Graph Representation Learning
Fatemeh Deldar - Mahdi Abadi - Mohammad Ebrahimifard
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution
Alireza Aghelan - Modjtaba Rouhani
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer
Javad Mirzapour Kaleybar - Hooman Saadat - Hooman Khaloo
An Evolutionary Approach with Surrogate Models for Feature Selection in Intrusion Detection Systems
Sadeq Moradi - Hadi Shahriar Shahhoseini
Dynamic Knowledge Enhanced Neural Fashion Trend Forecasting with Quantile Loss
Fatemeh Rooholamini - Reza Azmi - Mobina Khademhossein - Maral Zarvani
Multi-Layered Defense Against Modern Phishing: A Dual-Sandbox and CDR Approach
Mahdi Seyfipoor - Mohammad Mahdi Eskandari
Multi-source Ensemble Model for Scene Recognition
Amir Hossein Saleknia - Ahmad Ayatollahi
more
Samin Hamayesh - Version 43.7.0