Search Results for "ssim"

FFmpeg Quality Metrics is a Python-based tool that evaluates video quality by calculating objective metrics using FFmpeg. It supports widely used metrics such as PSNR, SSIM, VIF, MSAD, and VMAF, enabling detailed comparison between reference and distorted video files. The tool outputs both per-frame data and aggregated statistics like averages and standard deviation, making it useful for research, encoding optimization, and benchmarking. It also includes optional visualization features through an interactive web-based dashboard for analyzing results graphically. ...

Application developed in Java that allow the study of video streaming over Vehicular Networks (VANETs), including all the necessary tasks, namely: video encoding with HEVC, launching the simulations in parallel, generating graphs with the results, and analysing the received video quality with several Quality of Experience (QoE) metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), ... GatcomVideo is part of the Video Delivery Simulation Framework over Vehicular Networks (VDSF-VN), which is composed of several components: (1) an OMNeT++ project called "ppp_qos" (2) GatcomSUMO (3) GatcomVideo The "ppp_qos" project and the application GatcomSUMO are downloadable from different SourceForge projects. ...

Video Delivery Simulation Framework over Vehicular Networks (VDSF-VN) is a set of tools that allow the study of video streaming over Vehicular Networks (VANETs), including all the necessary tasks, namely: video encoding with HEVC, creating the network scenarios for SUMO & OMNeT++, launching the simulations in parallel, generating graphs with the results, analyzing the received video quality (PSNR, SSIM, ...). The framework is composed of several components: (1) an OMNeT++ project called "ppp_qos" (2) GatcomSUMO (3) GatcomVideo The applications GatcomSUMO and GatcomVideo are downloable from different SourceForge projects, and are developed in Java, so, they are platform independent, as well as the VDSF-VN framework. ...

Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix. We have released the trained model on BCI and LLVIP datasets. We host a competition for breast cancer immunohistochemistry image generation on Grand Challenge. Project pix2pix provides a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene, these can be pairs {HE, IHC}. Then we can learn to translate A(HE images)...

While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used perceptual metrics today, such as PSNR and SSIM, are simple, shallow functions, and fail to account for many nuances of human perception. Recently, the deep learning community has found that features of the VGG network trained on ImageNet classification has been remarkably useful as a training loss for image synthesis. But how perceptual are these so-called "perceptual losses"? ...

...QoE Monitor is currently developed by the Networking Research group of University of Modena and Reggio Emilia (http://www.unimore.it/). List of currently available features: - PSNR/SSIM computation for H.264 video transmission - G.711 audio transmission - Basic RTP emulation - Receiver side jitter estimation, according to RFC 3550 To do: - support new audio and video codecs - support new audio and video formats - develop objective audio evaluation metric(s) Update 02/27/2013: The latest archive has been updated to include some new features.

IQA is a C library for objectively measuring image/video quality. It implements many popular algorithms, such as MS-SSIM, MS-SSIM*, SIMM, MSE, and PSNR. It is designed to be fast, accurate, and reliable. All code is Valgrind-clean and unit tested.

A Java Library/Servlet/Service for on-the-fly scaling and efficient caching of images on web servers. Allows web authors to supply only a single (usually large) instance of any image, then refer to scaled images with urls like "myImage.jpg?width=120".

...Replays are deterministic and token-free, so your CI/CD bill doesn't scale with your test suite. Lastest ships three diff engines side-by-side — pixel (pixelmatch), structural (SSIM), and perceptual (Butteraugli) — so flaky pixel diffs stop crying wolf. A review dashboard tracks baselines, approvals, and audit history. Run it via Docker on your own infrastructure for unlimited screenshots and full data residency, or run on Lastest Cloud for managed CI runners. Built for solo founders, devs doing QA without a QA team, and SaaS teams that need an Applitools-class alternative without the $699+/mo invoice or the data-exfiltration problem.