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Found 21 papers, 7 papers with code
SCP: Spherical-Coordinate-based Learned Point Cloud Compression
In recent years, the task of learned point cloud compression has gained prominence.
Practical Commercial 5G Standalone (SA) Uplink Throughput Prediction
While the 5G New Radio (NR) network promises a huge uplift of the uplink throughput, the improvement can only be seen when the User Equipment (UE) is connected to the high-frequency millimeter wave (mmWave) band.
Learned Image Compression with Mixed Transformer-CNN Architectures
Most existing LIC methods are Convolutional Neural Networks-based (CNN-based) or Transformer-based, which have different advantages.
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Multistage Spatial Context Models for Learned Image Compression
The proposed method features a comparable decoding speed to Checkerboard while reaching the RD performance of Autoregressive and even also outperforming Autoregressive.
Pensieve 5G: Implementation of RL-based ABR Algorithm for UHD 4K/8K Content Delivery on Commercial 5G SA/NR-DC Network
While the rollout of the fifth-generation mobile network (5G) is underway across the globe with the intention to deliver 4K/8K UHD videos, Augmented Reality (AR), and Virtual Reality (VR) content to the mass amounts of users, the coverage and throughput are still one of the most significant issues, especially in the rural areas, where only 5G in the low-frequency band are being deployed.
ABCAS: Adaptive Bound Control of spectral norm as Automatic Stabilizer
Spectral Normalization is one of the best methods for stabilizing the training of Generative Adversarial Network.
Semantic Segmentation in Learned Compressed Domain
Most machine vision tasks (e. g., semantic segmentation) are based on images encoded and decoded by image compression algorithms (e. g., JPEG).
Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules
Lossless image compression is an essential research field in image compression.
Streaming-capable High-performance Architecture of Learned Image Compression Codecs
Learned image compression allows achieving state-of-the-art accuracy and compression ratios, but their relatively slow runtime performance limits their usage.
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module
Learned Image Compression (LIC) gradually became more and more famous in these years.
Q-LIC: Quantizing Learned Image Compression with Channel Splitting
Learned image compression (LIC) has reached a comparable coding gain with traditional hand-crafted methods such as VVC intra.
RSSI-CSI Measurement and Variation Mitigation with Commodity WiFi Device
In this paper, the relationship between RSSI and CSI is studied in detail and the measurement variation of amplitude and phase information is investigated by extensive experiments.
End-to-End Learned Image Compression with Quantized Weights and Activations
To our best knowledge, this is the first work to give a complete analysis on the coding gain and the memory cost for a quantized LIC network, which validates the feasibility of the hardware implementation.
Learned Video Compression with Residual Prediction and Loop Filter
To reduce the complexity, a light ResNet structure is used as the backbone for both RP-Net and LF-Net.
Fully Neural Network Mode Based Intra Prediction of Variable Block Size
As far as we know, this is the first work to explore a fully NM based framework for intra prediction, and we reach a better coding gain with a lower complexity compared with the previous work.
A QP-adaptive Mechanism for CNN-based Filter in Video Coding
Convolutional neural network (CNN)-based filters have achieved great success in video coding.
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
In this paper, we explore the remaining redundancy of recent learned compression algorithms.
Dual Learning-based Video Coding with Inception Dense Blocks
This method is mainly composed of two parts: intra prediction and reconstruction filtering.
Learning Image and Video Compression through Spatial-Temporal Energy Compaction
Experimental results demonstrate that our proposed image compression outperforms the latest image compression standard with MS-SSIM quality metric, and provides higher performance compared with state-of-the-art learning compression methods at high bit rates, which benefits from our spatial energy compaction approach.
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression
Image compression has been investigated for many decades.
Deep Convolutional AutoEncoder-based Lossy Image Compression
Image compression has been investigated as a fundamental research topic for many decades.
