Papers Video Compression
“Video Compression” 태그가 달린 논문 496편 · 필터 해제
Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks
Autonomous driving systems require extensive data collection schemes to cover the diverse scenarios needed for building a robust and safe system. The data volumes are in the order of Exabytes and have to be stored for a …
3D Object DetectionAutonomous Drivingobject-detectionObject Detection+1VQ-NeRV: A Vector Quantized Neural Representation for Videos
Implicit neural representations (INR) excel in encoding videos within neural networks, showcasing promise in computer vision tasks like video compression and denoising. INR-based approaches reconstruct video frames from …
DenoisingregressionVideo CompressionVideo InpaintingHierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement
Video compression artifacts arise due to the quantization operation in the frequency domain. The goal of video quality enhancement is to reduce compression artifacts and reconstruct a visually-pleasant result. In this wo…
QuantizationVideo CompressionChannel-wise Feature Decorrelation for Enhanced Learned Image Compression
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance. This approach is considered as the future of ima…
Image CompressionVideo CompressionImage Coding for Machines with Edge Information Learning Using Segment Anything
Image Coding for Machines (ICM) is an image compression technique for image recognition. This technique is essential due to the growing demand for image recognition AI. In this paper, we propose a method for ICM that foc…
Image CompressionVideo CompressionVariable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets
Achieving successful variable bitrate compression with computationally simple algorithms from a single end-to-end learned image or video compression model remains a challenge. Many approaches have been proposed, includin…
Image CompressionQuantizationVideo CompressionNERV++: An Enhanced Implicit Neural Video Representation
Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a l…
DecoderVideo CompressionNeural Video Compression with Feature Modulation
The emerging conditional coding-based neural video codec (NVC) shows superiority over commonly-used residual coding-based codec and the latest NVC already claims to outperform the best traditional codec. However, there s…
BlockingQuantizationVideo CompressionResolution-Agnostic Neural Compression for High-Fidelity Portrait Video Conferencing via Implicit Radiance Fields
Video conferencing has caught much more attention recently. High fidelity and low bandwidth are two major objectives of video compression for video conferencing applications. Most pioneering methods rely on classic video…
Video CompressionDistributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving
The metaverse is a virtual space that combines physical and digital elements, creating immersive and connected digital worlds. For autonomous mobility, it enables new possibilities with edge computing and digital twins (…
3D Scene ReconstructionAutonomous DrivingAutonomous VehiclesDecoder+3Analysis of Neural Video Compression Networks for 360-Degree Video Coding
With the increasing efforts of bringing high-quality virtual reality technologies into the market, efficient 360-degree video compression gains in importance. As such, the state-of-the-art H.266/VVC video coding standard…
ERPVideo CompressionExtreme Video Compression with Pre-trained Diffusion Models
Diffusion models have achieved remarkable success in generating high quality image and video data. More recently, they have also been used for image compression with high perceptual quality. In this paper, we present a n…
DecoderImage CompressionVideo CompressionA Neural-network Enhanced Video Coding Framework beyond ECM
In this paper, a hybrid video compression framework is proposed that serves as a demonstrative showcase of deep learning-based approaches extending beyond the confines of traditional coding methodologies. The proposed hy…
Deep LearningVideo CompressionMotion-Adaptive Inference for Flexible Learned B-Frame Compression
While the performance of recent learned intra and sequential video compression models exceed that of respective traditional codecs, the performance of learned B-frame compression models generally lag behind traditional B…
Video CompressionSandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
We propose sandwiching standard image and video codecs between pre- and post-processing neural networks. The networks are jointly trained through a differentiable codec proxy to minimize a given rate-distortion loss. Thi…
Video CompressionImmersive Video Compression using Implicit Neural Representations
Recent work on implicit neural representations (INRs) has evidenced their potential for efficiently representing and encoding conventional video content. In this paper we, for the first time, extend their application to …
Video CompressionEfficient Dynamic-NeRF Based Volumetric Video Coding with Rate Distortion Optimization
Volumetric videos, benefiting from immersive 3D realism and interactivity, hold vast potential for various applications, while the tremendous data volume poses significant challenges for compression. Recently, NeRF has d…
NeRFVideo CompressionUCVC: A Unified Contextual Video Compression Framework with Joint P-frame and B-frame Coding
This paper presents a learned video compression method in response to video compression track of the 6th Challenge on Learned Image Compression (CLIC), at DCC 2024.Specifically, we propose a unified contextual video comp…
Image CompressionVideo CompressionLVC-LGMC: Joint Local and Global Motion Compensation for Learned Video Compression
Existing learned video compression models employ flow net or deformable convolutional networks (DCN) to estimate motion information. However, the limited receptive fields of flow net and DCN inherently direct their atten…
Motion CompensationVideo CompressionA Neural Enhancement Post-Processor with a Dynamic AV1 Encoder Configuration Strategy for CLIC 2024
At practical streaming bitrates, traditional video compression pipelines frequently lead to visible artifacts that degrade perceptual quality. This submission couples the effectiveness of a neural post-processor with a d…
Video Compression