Papers Video Compression
“Video Compression” 태그가 달린 논문 496편 · 필터 해제
Learn to Compress (LtC): Efficient Learning-based Streaming Video Analytics
Video analytics are often performed as cloud services in edge settings, mainly to offload computation, and also in situations where the results are not directly consumed at the video sensors. Sending high-quality video d…
Video CompressionLearned Scalable Video Coding For Humans and Machines
Video coding has traditionally been developed to support services such as video streaming, videoconferencing, digital TV, and so on. The main intent was to enable human viewing of the encoded content. However, with the a…
Object Detectionvehicle detectionVideo CompressionSpatially-Adaptive Learning-Based Image Compression with Hierarchical Multi-Scale Latent Spaces
Adaptive block partitioning is responsible for large gains in current image and video compression systems. This method is able to compress large stationary image areas with only a few symbols, while maintaining a high le…
Image CompressionVideo CompressionOffline and Online Optical Flow Enhancement for Deep Video Compression
Video compression relies heavily on exploiting the temporal redundancy between video frames, which is usually achieved by estimating and using the motion information. The motion information is represented as optical flow…
Motion EstimationOptical Flow EstimationVideo CompressionPredictive Coding For Animation-Based Video Compression
We address the problem of efficiently compressing video for conferencing-type applications. We build on recent approaches based on image animation, which can achieve good reconstruction quality at very low bitrate by rep…
Image AnimationVideo CompressionColor Learning for Image Compression
Deep learning based image compression has gained a lot of momentum in recent times. To enable a method that is suitable for image compression and subsequently extended to video compression, we propose a novel deep learni…
Deep LearningImage CompressionVideo CompressionMulti-Scale Deformable Alignment and Content-Adaptive Inference for Flexible-Rate Bi-Directional Video Compression
The lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models. This paper advances the state-of-the-art by proposing an adap…
Motion CompensationVideo CompressionHiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs. In this context, Implicit Neural Representations (INRs) have previously b…
Model CompressionQuantizationVideo CompressionVideo Compression with Arbitrary Rescaling Network
Most video platforms provide video streaming services with different qualities, and the quality of the services is usually adjusted by the resolution of the videos. So high-resolution videos need to be downsampled for co…
Video CompressionVVC Extension Scheme for Object Detection Using Contrast Reduction
In recent years, video analysis using Artificial Intelligence (AI) has been widely used, due to the remarkable development of image recognition technology using deep learning. In 2019, the Moving Picture Experts Group (M…
DecoderObjectobject-detectionObject Detection+1On the Choice of Perception Loss Function for Learned Video Compression
We study causal, low-latency, sequential video compression when the output is subjected to both a mean squared-error (MSE) distortion loss as well as a perception loss to target realism. Motivated by prior approaches, we…
DecoderVideo CompressionLearned Wavelet Video Coding using Motion Compensated Temporal Filtering
We present an end-to-end trainable wavelet video coder based on motion-compensated temporal filtering (MCTF). Thereby, we introduce a different coding scheme for learned video compression, which is currently dominated by…
Video CompressionSR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate Reduction
In recent years, there has been significant interest in Super-Resolution (SR), which focuses on generating a high-resolution image from a low-resolution input. Deep learning-based methods for super-resolution have been p…
Super-ResolutionVideo CompressionVideo Quality AssessmentExpanding Synthetic Real-World Degradations for Blind Video Super Resolution
Video super-resolution (VSR) techniques, especially deep-learning-based algorithms, have drastically improved over the last few years and shown impressive performance on synthetic data. However, their performance on real…
Super-ResolutionVideo CompressionVideo Super-ResolutionThe Bjøntegaard Bible -- Why your Way of Comparing Video Codecs May Be Wrong
In this paper, we provide an in-depth assessment on the Bj{\o}ntegaard Delta. We construct a large data set of video compression performance comparisons using a diverse set of metrics including PSNR, VMAF, bitrate, and p…
QuantizationSSIMVideo CompressionPerceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method
Recent years have witnessed an exponential increase in the demand for face video compression, and the success of artificial intelligence has expanded the boundaries beyond traditional hybrid video coding. Generative codi…
Video CompressionVideo Quality AssessmentVisual Question Answering (VQA)DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos
Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual depend…
Video CompressionOne Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment
The Visual Multimethod Assessment Fusion (VMAF) algorithm has recently emerged as a state-of-the-art approach to video quality prediction, that now pervades the streaming and social media industry. However, since VMAF re…
AllComputational EfficiencyVideo CompressionVideo Quality AssessmentHNeRV: A Hybrid Neural Representation for Videos
Implicit neural representations store videos as neural networks and have performed well for various vision tasks such as video compression and denoising. With frame index or positional index as input, implicit representa…
DecoderDenoisingregressionVideo Compression+2MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding
Learning-based video compression has been extensively studied over the past years, but it still has limitations in adapting to various motion patterns and entropy models. In this paper, we propose multi-mode video compre…
BenchmarkingMS-SSIMOptical Flow EstimationPrediction+3