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Found 23 papers, 8 papers with code
Deep Convolutional AutoEncoder-based Lossy Image Compression
Image compression has been investigated as a fundamental research topic for many decades.
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression
Image compression has been investigated for many decades.
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.
Dual Learning-based Video Coding with Inception Dense Blocks
This method is mainly composed of two parts: intra prediction and reconstruction filtering.
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
In this paper, we explore the remaining redundancy of recent learned compression algorithms.
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval
For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1, 236 human-paraphrased queries while the latter contains ~32 human-annotated FAQ items for each query.
A QP-adaptive Mechanism for CNN-based Filter in Video Coding
Convolutional neural network (CNN)-based filters have achieved great success in video coding.
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.
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.
Learned Image Compression with Separate Hyperprior Decoders
Learned image compression techniques have achieved considerable development in recent years.
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.
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.
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module
Learned Image Compression (LIC) gradually became more and more famous in these years.
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.
Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules
Lossless image compression is an essential research field in image compression.
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).
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.
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.
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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Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts
For ICM, developing a unified codec to reduce information redundancy while empowering the compressed features to support various vision tasks is very important, which inevitably faces two core challenges: 1) How should the compression strategy be adjusted based on the downstream tasks?
SCP: Spherical-Coordinate-based Learned Point Cloud Compression
In recent years, the task of learned point cloud compression has gained prominence.
Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation
In recent years, end-to-end learnt video codecs have demonstrated their potential to compete with conventional coding algorithms in term of compression efficiency.
Attack and Defense Analysis of Learned Image Compression
Our experiments compare the effects of different dimensions such as attack methods, models, qualities, and targets, concluding that in the worst case, there is a 61. 55% decrease in PSNR or a 19. 15 times increase in bpp under the PGD attack.
