DSSIM: a structural similarity index for floating-point data
- URL: http://arxiv.org/abs/2202.02616v2
- Date: Sun, 19 Mar 2023 20:45:27 GMT
- Title: DSSIM: a structural similarity index for floating-point data
- Authors: Allison H. Baker and Alexander Pinard and Dorit M. Hammerling
- Abstract summary: We propose an alternative to the popular SSIM that can be applied directly to the floating point data, which we refer to as the Data SSIM (DSSIM)
While we demonstrate the usefulness of the DSSIM in the context of evaluating differences due to lossy compression on large volumes of simulation data, the DSSIM may prove useful for many other applications involving simulation or image data.
- Score: 68.8204255655161
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Data visualization is a critical component in terms of interacting with
floating-point output data from large model simulation codes. Indeed,
postprocessing analysis workflows on simulation data often generate a large
number of images from the raw data, many of which are then compared to each
other or to specified reference images. In this image-comparison scenario,
image quality assessment (IQA) measures are quite useful, and the Structural
Similarity Index (SSIM) continues to be a popular choice. However, generating
large numbers of images can be costly, and plot-specific (but data independent)
choices can affect the SSIM value. A natural question is whether we can apply
the SSIM directly to the floating-point simulation data and obtain an
indication of whether differences in the data are likely to impact a visual
assessment, effectively bypassing the creation of a specific set of images from
the data. To this end, we propose an alternative to the popular SSIM that can
be applied directly to the floating point data, which we refer to as the Data
SSIM (DSSIM). While we demonstrate the usefulness of the DSSIM in the context
of evaluating differences due to lossy compression on large volumes of
simulation data from a popular climate model, the DSSIM may prove useful for
many other applications involving simulation or image data.
Related papers
- CSIM: A Copula-based similarity index sensitive to local changes for Image quality assessment [2.3874115898130865]
Image similarity metrics play an important role in computer vision applications, as they are used in image processing, computer vision and machine learning.
Existing metrics, such as PSNR, MSE, SSIM, ISSM and FSIM, often face limitations in terms of either speed, complexity or sensitivity to small changes in images.
A novel image similarity metric, namely CSIM, that combines real-time while being sensitive to subtle image variations is investigated in this paper.
arXiv Detail & Related papers (2024-10-02T10:46:05Z) - SimMAT: Exploring Transferability from Vision Foundation Models to Any Image Modality [136.82569085134554]
Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact.
It is extremely challenging for sensors in many different fields to collect similar scales of natural images to train strong foundation models.
This work presents a simple and effective framework SimMAT to study an open problem: the transferability from vision foundation models trained on natural RGB images to other image modalities of different physical properties.
arXiv Detail & Related papers (2024-09-12T14:38:21Z) - NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking [65.24988062003096]
We present NAVSIM, a framework for benchmarking vision-based driving policies.
Our simulation is non-reactive, i.e., the evaluated policy and environment do not influence each other.
NAVSIM enabled a new competition held at CVPR 2024, where 143 teams submitted 463 entries, resulting in several new insights.
arXiv Detail & Related papers (2024-06-21T17:59:02Z) - SimGen: Simulator-conditioned Driving Scene Generation [50.03358485083602]
We introduce a simulator-conditioned scene generation framework called SimGen.
SimGen learns to generate diverse driving scenes by mixing data from the simulator and the real world.
It achieves superior generation quality and diversity while preserving controllability based on the text prompt and the layout pulled from a simulator.
arXiv Detail & Related papers (2024-06-13T17:58:32Z) - sim2real: Cardiac MR Image Simulation-to-Real Translation via
Unsupervised GANs [0.4433315630787158]
We provide image simulation on virtual XCAT subjects with varying anatomies.
We propose sim2real translation network to improve image realism.
Our usability experiments suggest that sim2real data exhibits a good potential to augment training data and boost the performance of a segmentation algorithm.
arXiv Detail & Related papers (2022-08-09T16:06:06Z) - Task2Sim : Towards Effective Pre-training and Transfer from Synthetic
Data [74.66568380558172]
We study the transferability of pre-trained models based on synthetic data generated by graphics simulators to downstream tasks.
We introduce Task2Sim, a unified model mapping downstream task representations to optimal simulation parameters.
It learns this mapping by training to find the set of best parameters on a set of "seen" tasks.
Once trained, it can then be used to predict best simulation parameters for novel "unseen" tasks in one shot.
arXiv Detail & Related papers (2021-11-30T19:25:27Z) - A Hitchhiker's Guide to Structural Similarity [40.567747702628076]
The Structural Similarity (SSIM) Index is a very widely used image/video quality model.
We studied and compared the functions and performances of popular and widely used implementations of SSIM.
We have arrived at a collection of recommendations on how to use SSIM most effectively.
arXiv Detail & Related papers (2021-01-16T02:51:06Z) - Recurrent convolutional neural network for the surrogate modeling of
subsurface flow simulation [0.0]
We propose to combine SegNet with ConvLSTM layers for the surrogate modeling of numerical flow simulation.
Results show that the proposed method improves the performance of SegNet based surrogate model remarkably when the output of the simulation is time series data.
arXiv Detail & Related papers (2020-10-08T09:34:48Z) - Fed-Sim: Federated Simulation for Medical Imaging [131.56325440976207]
We introduce a physics-driven generative approach that consists of two learnable neural modules.
We show that our data synthesis framework improves the downstream segmentation performance on several datasets.
arXiv Detail & Related papers (2020-09-01T19:17:46Z) - ML-SIM: A deep neural network for reconstruction of structured
illumination microscopy images [0.0]
Structured illumination microscopy (SIM) has become an important technique for optical super-resolution imaging.
Here we propose a versatile reconstruction method, ML-SIM, which makes use of machine learning.
ML-SIM is thus robust to noise and irregularities in the illumination patterns of the raw SIM input frames.
arXiv Detail & Related papers (2020-03-24T18:42:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.