Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds https://arxiv.org/abs/1905.03691 Sampling layer 在G-PCC中,基于八叉树的几何编码根据量化尺度来控制有损几何压缩,设输入点云为: G-PCC编码器的量化计算如下: 其中 和 是用户人工定义的参数。 量化后,将有许多重复点共享...
Image compression is a crown field of computer vision, and deep learning is gradually being used for image compression & decompression tasks. The compression rate of the lossy compression algorithm is higher than lossless compression but the main disadvantage of lossy compression is the loss of data...
The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network
Variational Autoencoders (VAEs), as a form of deep generative model, have been widely used in recent years, and shown great great peformance in a number of different domains, including image generation and anomaly detection, etc.. This paper aims to explore neural network model compression meth...
To address these limitations, in this paper we propose Hyperspectral Compression Transformer (HyCoT) that is a transformer-based autoencoder for pixelwise HSI compression. Additionally, we apply a simple yet effective training set reduction approach to accelerate the training process. Experimental ...
(\mathbf{p}_{ \text{continuous}}\)) are processed for compression into the latent spacezand subsequent reconstruction. Discrete parameters (\(\mathbf{p}_{\text{discrete}}\)), such as topology or the maximum AC line current, are concatenated to the compressed latent spacez, which is then ...
Ichimoku-based features have gained significant attention in financial market analysis due to their ability to capture essential market signals and patterns. This significant compression retains essential patterns related to trends, support/resistance levels, and trading signals. The reduced dimensionality ...
As illustrated inFigure 1, in a VAE, the architecture and parameters for each layer are designed to achieve a balance between efficient data compression and accurate reconstruction. The VAE consists of two main components: the encoder and the decoder. The encoder reduces the input data to a low...
We introduce ADMM-pruned Compressive AutoEncoder (CAE-ADMM) that uses Alternative Direction Method of Multipliers (ADMM) to optimize the trade-off between distortion and efficiency of lossy image compression. Specifically, ADMM in our method is to promote sparsity to implicitly optimize the bitrate, ...
reconstruction. Our experiments show that the improved patch-based autoencoder outperforms the state-of-the-art in terms of rate-distortion performance, on both sparse and large-scale point clouds. More importantly, our method can maintain a short compression time while ensuring the reconstruction ...