Papers Video Compression
“Video Compression” 태그가 달린 논문 496편 · 필터 해제
Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement
Video compression is a central feature of the modern internet powering technologies from social media to video conferencing. While video compression continues to mature, for many compression settings, quality loss is sti…
QuantizationVideo CompressionEnhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing
The rapid development of multimedia and communication technology has resulted in an urgent need for high-quality video streaming. However, robust video streaming under fluctuating network conditions and heterogeneous cli…
Deep Reinforcement LearningSuper-ResolutionVideo CompressionVideo EnhancementNeural Residual Flow Fields for Efficient Video Representations
Neural fields have emerged as a powerful paradigm for representing various signals, including videos. However, research on improving the parameter efficiency of neural fields is still in its early stages. Even though neu…
Video CompressionMicrodosing: Knowledge Distillation for GAN based Compression
Recently, significant progress has been made in learned image and video compression. In particular the usage of Generative Adversarial Networks has lead to impressive results in the low bit rate regime. However, the mode…
Knowledge DistillationVideo CompressionLearning True Rate-Distortion-Optimization for End-To-End Image Compression
Even though rate-distortion optimization is a crucial part of traditional image and video compression, not many approaches exist which transfer this concept to end-to-end-trained image compression. Most frameworks contai…
Image CompressionMS-SSIMSSIMVideo CompressionLearning Based Multi-Modality Image and Video Compression
Multi-modality (i.e., multi-sensor) data is widely used in various vision tasks for more accurate or robust perception. However, the increased data modalities bring new challenges for data storage and transmission. T…
Data CompressionVideo CompressionLSVC: A Learning-Based Stereo Video Compression Framework
In this work, we propose the first end-to-end optimized framework for compressing automotive stereo videos (i.e., stereo videos from autonomous driving applications) from both left and right views. Specifically, when…
Autonomous DrivingMotion CompensationVideo CompressionSFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences
We present a dataset that contains object annotations with unique object identities (IDs) for the High Efficiency Video Coding (HEVC) v1 Common Test Conditions (CTC) sequences. Ground-truth annotations for 13 sequences w…
ObjectObject TrackingVideo CompressionLearning Cross-Scale Weighted Prediction for Efficient Neural Video Compression
Neural video codecs have demonstrated great potential in video transmission and storage applications. Existing neural hybrid video coding approaches rely on optical flow or Gaussian-scale flow for prediction, which canno…
Motion CompensationOptical Flow EstimationPredictionQuantization+1End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
Conventional video compression (VC) methods are based on motion compensated transform coding, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually d…
Motion EstimationMS-SSIMQuantizationSSIM+1Generalized Difference Coder: A Novel Conditional Autoencoder Structure for Video Compression
Motion compensated inter prediction is a common component of all video coders. The concept was established in traditional hybrid coding and successfully transferred to learning-based video compression. To compress the re…
Video CompressionNeural Weight Step Video Compression
A variety of compression methods based on encoding images as weights of a neural network have been recently proposed. Yet, the potential of similar approaches for video compression remains unexplored. In this work, we su…
Video CompressionViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing
This paper presents a deep learning-based video compression framework (ViSTRA3). The proposed framework intelligently adapts video format parameters of the input video before encoding, subsequently employing a CNN at the…
DecoderVideo CompressionLearning-Based Video Coding with Joint Deep Compression and Enhancement
The end-to-end learning-based video compression has attracted substantial attentions by paving another way to compress video signals as stacked visual features. This paper proposes an efficient end-to-end deep video code…
Generative Adversarial NetworkMotion CompensationMotion EstimationMS-SSIM+2Data-independent Low-complexity KLT Approximations for Image and Video Coding
The Karhunen-Lo\`eve transform (KLT) is often used for data decorrelation and dimensionality reduction. The KLT is able to optimally retain the signal energy in only few transform components, being mathematically suitabl…
Dimensionality ReductionVideo CompressionTemporal Context Mining for Learned Video Compression
We address end-to-end learned video compression with a special focus on better learning and utilizing temporal contexts. For temporal context mining, we propose to store not only the previously reconstructed frames, but …
DecoderMS-SSIMSSIMVideo CompressionDeepWiVe: Deep-Learning-Aided Wireless Video Transmission
We present DeepWiVe, the first-ever end-to-end joint source-channel coding (JSCC) video transmission scheme that leverages the power of deep neural networks (DNNs) to directly map video signals to channel symbols, combin…
Deep LearningMS-SSIMReinforcement Learning (RL)SSIM+1Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
We introduce a video compression algorithm based on instance-adaptive learning. On each video sequence to be transmitted, we finetune a pretrained compression model. The optimal parameters are transmitted to the receiver…
Video CompressionVersatile Learned Video Compression
Learned video compression methods have demonstrated great promise in catching up with traditional video codecs in their rate-distortion (R-D) performance. However, existing learned video compression schemes are limited b…
Motion CompensationMS-SSIMPredictionSSIM+1NeRV: Neural Representations for Videos
We propose a novel neural representation for videos (NeRV) which encodes videos in neural networks. Unlike conventional representations that treat videos as frame sequences, we represent videos as neural networks taking …
DenoisingNeural Network CompressionVideo CompressionVideo Denoising+1