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Found 24 papers, 7 papers with code
Task-Aware Encoder Control for Deep Video Compression
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task.
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage.
MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model
With the evolution of storage and communication protocols, ultra-low bitrate image compression has become a highly demanding topic.
Efficient 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.
Implicit-explicit Integrated Representations for Multi-view Video Compression
To further enhance the reconstruction quality from the INR codec, we leverage the high-quality reconstructed frames from the explicit codec to achieve inter-view compensation.
Non-Semantics Suppressed Mask Learning for Unsupervised Video Semantic Compression
Most video compression methods aim to improve the decoded video visual quality, instead of particularly guaranteeing the semantic-completeness, which deteriorates downstream video analysis tasks, e. g., action recognition.
Content Adaptive Latents and Decoder for Neural Image Compression
Although several content adaptive methods have been proposed by updating the encoder-side components, the adaptability of both latents and the decoder is not well exploited.
Coarse-to-fine Deep Video Coding with Hyperprior-guided Mode Prediction
The previous deep video compression approaches only use the single scale motion compensation strategy and rarely adopt the mode prediction technique from the traditional standards like H. 264/H. 265 for both motion and residual compression.
Preprocessing Enhanced Image Compression for Machine Vision
Specifically, we propose a neural preprocessing module before the encoder to maintain the useful semantic information for the downstream tasks and suppress the irrelevant information for bitrate saving.
A Coding Framework and Benchmark towards Compressed Video Understanding
However, in real-world scenarios, the videos are first compressed before the transportation and then decompressed for understanding.
LSVC: 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.
Learning Based Multi-Modality Image and Video Compression
Specifically, given the image in the reference modality (e. g., the infrared image), we use the channel-wise alignment module to produce the aligned features based on the affine transform.
Self-Conditioned Probabilistic Learning of Video Rescaling
After optimization, the downscaled video by our framework preserves more meaningful information, which is beneficial for both the upscaling step and the downstream tasks, e. g., video action recognition task.
CBANet: Towards Complexity and Bitrate Adaptive Deep Image Compression using a Single Network
Furthermore, to achieve variable bitrate decoding with one single decoder, we propose a bitrate adaptive module to project the representation from a base bitrate to the expected representation at a target bitrate for transmission.
FVC: A New Framework towards Deep Video Compression in Feature Space
In this work, we propose a feature-space video coding network (FVC) by performing all major operations (i. e., motion estimation, motion compression, motion compensation and residual compression) in the feature space.
VoxelContext-Net: An Octree based Framework for Point Cloud Compression
In this paper, we propose a two-stage deep learning framework called VoxelContext-Net for both static and dynamic point cloud compression.
Direct Depth Learning Network for Stereo Matching
To make the Adaptive-Grained Depth Refinement stage robust to the coarse depth and adaptive to the depth range of the points, the Granularity Uncertainty is introduced to Adaptive-Grained Depth Refinement stage.
Improving Deep Video Compression by Resolution-adaptive Flow Coding
In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder.
Scope Head for Accurate Localization in Object Detection
Existing anchor-based and anchor-free object detectors in multi-stage or one-stage pipelines have achieved very promising detection performance.
Content Adaptive and Error Propagation Aware Deep Video Compression
Therefore, the encoder is adaptive to different video contents and achieves better compression performance by reducing the domain gap between the training and testing datasets.
A Unified End-to-End Framework for Efficient Deep Image Compression
Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
Learning 3D Human Shape and Pose from Dense Body Parts
Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods.
Ranked #75 on
3D Human Pose Estimation
on 3DPW
(MPJPE metric)
3D human pose and shape estimation
3D Human Reconstruction
+3
DVC: An End-to-end Deep Video Compression Framework
Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information.
Deep Kalman Filtering Network for Video Compression Artifact Reduction
In this paper, we model the video artifact reduction task as a Kalman filtering procedure and restore decoded frames through a deep Kalman filtering network.
