Binary Probability Model for Learning Based Image Compression

Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction

This work extends the multiscale structure originally developed for point cloud geometry compression to point cloud attribute compression. To losslessly encode the attribute while maintaining a low bitrate, accurate probability prediction is critical. With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency. Specifically, we first generate multiscale attribute tensors through average pooling, by which, for any two consecutive scales, the decoded lower-scale att…

Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model

The past few years have witnessed increasing interests in applying deep learning to video compression. However, the existing approaches compress a video frame with only a few number of reference frames, which limits their ability to fully exploit the temporal correlation among video frames. To overcome this shortcoming, this paper proposes a Recurrent Learned Video Compression (RLVC) approach with the Recurrent Auto-Encoder (RAE) and Recurrent Probability Model (RPM). Specifically, the RAE employs recurrent cells in both the encoder and decoder. As such, the temporal information in a large ran…

Lossless Image Compression through Super-Resolution

We introduce a simple and efficient lossless image compression algorithm. We store a low resolution version of an image as raw pixels, followed by several iterations of lossless super-resolution. For lossless super-resolution, we predict the probability of a high-resolution image, conditioned on the low-resolution input, and use entropy coding to compress this super-resolution operator. Super-Resolution based Compression (SReC) is able to achieve state-of-the-art compression rates with practical runtimes on large datasets. Code is available online at https://github.com/caoscott/SReC.

Entroformer: A Transformer-based Entropy Model for Learned Image Compression

One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules. Previous works build entropy models upon convolutional neural networks which are inefficient in capturing global dependencies. In this work, we propose a novel transformer-based entropy model, termed Entroformer, to capture long-range dependencies in probability distribution estimation effectively and efficiently. Different from vision transformers in image classification, the Entroformer is highly …

面向点云属性压缩的带动态似然区间的广义高斯熵模型

Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals

针对现有点云属性压缩熵模型概率估计不准、算术编码用固定似然区间的问题，提出带形状参数的广义高斯熵模型与可动态调整区间的MED判别器，显著提升VAE类模型的率失真性能，还可推广至图像视频压缩。

通过基于注意力的子节点数预测增强用于点云几何压缩的基于八叉树的上下文模型

Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction

针对点云几何压缩中八叉树上下文模型交叉熵损失无法准确衡量分布差异的问题，提出基于注意力的子节点数预测模块，通过预测占用子节点数并映射为8维向量辅助概率预测，提升了编码效率。

MMVC：基于块预测模式选择和密度自适应熵编码的学习型多模式视频压缩

MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding

提出多模式视频压缩框架MMVC，通过块级预测模式选择和密度自适应双模式熵编码，适应不同运动模式并提升压缩率，在多项基准上表现优异。

Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression

This study develops a unified Point Cloud Geometry (PCG) compression method through the processing of multiscale sparse tensor-based voxelized PCG. We call this compression method SparsePCGC. The proposed SparsePCGC is a low complexity solution because it only performs the convolutions on sparsely-distributed Most-Probable Positively-Occupied Voxels (MP-POV). The multiscale representation also allows us to compress scale-wise MP-POVs by exploiting cross-scale and same-scale correlations extensively and flexibly. The overall compression efficiency highly depends on the accuracy of estimated occ…

基于掩码采样与由粗到细自回归的深度无损图像压缩

Deep Lossless Image Compression via Masked Sampling and Coarse-to-Fine Auto-Regression

提出融合有损重建与渐进残差压缩的深度无损图像压缩方法，通过 $T$ 次掩码采样和由粗到细自回归融合多向上下文，在多数据集上取得相当压缩率、更快且更灵活。

Learned Image Compression with Soft Bit-based Rate-Distortion Optimization

This paper introduces the notion of soft bits to address the rate-distortion optimization for learning-based image compression. Recent methods for such compression train an autoencoder end-to-end with an objective to strike a balance between distortion and rate. They are faced with the zero gradient issue due to quantization and the difficulty of estimating the rate accurately. Inspired by soft quantization, we represent quantization indices of feature maps with differentiable soft bits. This allows us to couple tightly the rate estimation with context-adaptive binary arithmetic coding. It als…

Soft Compression for Lossless Image Coding

Soft compression is a lossless image compression method, which is committed to eliminating coding redundancy and spatial redundancy at the same time by adopting locations and shapes of codebook to encode an image from the perspective of information theory and statistical distribution. In this paper, we propose a new concept, compressible indicator function with regard to image, which gives a threshold about the average number of bits required to represent a location and can be used for revealing the performance of soft compression. We investigate and analyze soft compression for binary image, …

利用隐式局部似然模型提升神经图像压缩的统计保真度

Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models

本文针对神经图像压缩中低比特率下重建图像统计失真问题，提出一种以VQ-VAE量化局部表示为条件的非二值判别器，在CLIC2020等数据集上以比HiFiC少30-40%比特数实现相同FID。