lossy专题

数据流基本问题--确定频繁元素(二)lossy counting和sticky sampling

我们之前在数据流基本问题--确定频繁元素(一)中提到了频繁元素的一个计算问题(找出出现次数超过m/k的元素),里面的算法返回的结果里肯定包含出现次数超过m/k的元素,但是也可能包含不超过m/k的元素(false positive)。对于这个缺点,必须得进行额外一次的重新扫描,以确定最终答案。我们只允许进行一次的扫描,那么该怎么去做呢?这里我们简单讨论下lossy counting算法和sticky

【点云阅读笔记】LEARNING CONVOLUTIONAL TRANSFORMS FOR LOSSY POINT CLOUD GEOMETRY COMPRESSION

阅读报告——LEARNING CONVOLUTIONAL TRANSFORMS FOR LOSSY POINT CLOUD GEOMETRY COMPRESSION Worth noticing in Introduction 质量评价衡量: D1 and D2 D1:计算重建点和K近邻之间的MSE D2:计算重建点和超平面之间的MSE Main Idea 对于基于传统八叉树模型

Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds

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 X s h i f t X

This warning:Lossy conversion from float64 to uint8. Range [0, 1].

在图像预处理/图片处理的过程中,会带有把uint8图像转换成float64类型的警告提示:Lossy conversion from float64 to uint8. Range [0, 1]. Convert image to uint8 prior to saving to suppress this warning. 这时只要添加一行代码即可: dst = (dst*255.0).as

关于出现incompatible types: possible lossy conversion from long to int错误(类型转化错误)

场景: leetcode第七题: 7、整数反转 给你一个 32 位的有符号整数 x ,返回将 x 中的数字部分反转后的结果。 如果反转后整数超过 32 位的有符号整数的范围 [−231, 231 − 1] ,就返回 0。 假设环境不允许存储 64 位整数(有符号或无符号)。 来源:力扣(LeetCode) 链接:https://leetcode-cn.com/problems/rever

High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation

High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information Aggregation 文章目录 High-Efficiency Lossy Image Coding Through Adaptive Neighborhood Information AggregationAbstratI. In

Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection 翻译(有PPT)自留学习

原论文链接:2212.02303.pdf (arxiv.org) 数据集链接:SKAB - Skoltech Anomaly Benchmark | Kaggle 建议先了解:Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. Variational image compression

Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication

Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication

IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression

文章目录 IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression采样点的octree编码集成的压缩和训练过程熵编码的可学习上下文模型对抗学习均匀性度量实验结果 IPDAE: Improved Patch-Based Deep Autoencoder for Lo