On Energy Compaction of 2D Saab Image Transforms

Explainable Machine Learning based Transform Coding for High Efficiency Intra Prediction

Machine learning techniques provide a chance to explore the coding performance potential of transform. In this work, we propose an explainable transform based intra video coding to improve the coding efficiency. Firstly, we model machine learning based transform design as an optimization problem of maximizing the energy compaction or decorrelation capability. The explainable machine learning based transform, i.e., Subspace Approximation with Adjusted Bias (Saab) transform, is analyzed and compared with the mainstream Discrete Cosine Transform (DCT) on their energy compaction and decorrelation …

Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI

Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function. Accurate segmentation of cardiac structures from cine MRI is a crucial step for early diagnosis and prognosis of CVD, and has been greatly improved with convolutional neural networks (CNN). There, however, are a number of limitations identified in CNN models, such as limited interpretability and high complexity, thus limiting their use in clinical practice. In this work, to address the limitations, we propose a lightweight and i…

学习型图像压缩中非线性变换的解耦训练研究

On Disentangled Training for Nonlinear Transform in Learned Image Compression

针对学习型图像压缩训练收敛慢的问题，论文揭示能量压缩的两部分构成，并提出AuxT及WLSs以解耦非线性变换训练。

基于块的图像压缩中DCT、Hadamard和PCA的性能分析

Performance Analysis of DCT, Hadamard, and PCA in Block-Based Image Compression

本文通过实验比较DCT、Hadamard和PCA在不同块大小和压缩率下的性能，发现PCA在块维度较大时优于固定变换，而DCT在标准块大小和低比特率下仍表现优异。

余弦变换的二元相位恢复：基于局部曲率最小化

Binary Phase Retrieval of Cosine Transforms via Local Curvature Minimization

本文针对余弦变换的二元相位恢复问题，提出通过最小化重建变换的曲率来求解，实验表明该方法可稳定重建图像，多尺度结构相似性指数达0.95，且求解时间近似线性增长。

Unsupervised Data-Driven Nuclei Segmentation For Histology Images

An unsupervised data-driven nuclei segmentation method for histology images, called CBM, is proposed in this work. CBM consists of three modules applied in a block-wise manner: 1) data-driven color transform for energy compaction and dimension reduction, 2) data-driven binarization, and 3) incorporation of geometric priors with morphological processing. CBM comes from the first letter of the three modules - "Color transform", "Binarization" and "Morphological processing". Experiments on the MoNuSeg dataset validate the effectiveness of the proposed CBM method. CBM outperforms all other unsuper…