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Found 8 papers, 4 papers with code
Bitrate Ladder Prediction Methods for Adaptive Video Streaming: A Review and Benchmark
A key component of HAS is the bitrate ladder, which provides the encoding parameters (e. g., bitrate-resolution pairs) to encode the source video.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images.
2BiVQA: Double Bi-LSTM based Video Quality Assessment of UGC Videos
Recently, with the growing popularity of mobile devices as well as video sharing platforms (e. g., YouTube, Facebook, TikTok, and Twitch), User-Generated Content (UGC) videos have become increasingly common and now account for a large portion of multimedia traffic on the internet.
Ranked #9 on
Video Quality Assessment
on LIVE-VQC
(using extra training data)
Detect and Defense Against Adversarial Examples in Deep Learning using Natural Scene Statistics and Adaptive Denoising
Despite the enormous performance of deepneural networks (DNNs), recent studies have shown theirvulnerability to adversarial examples (AEs), i. e., care-fully perturbed inputs designed to fool the targetedDNN.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Adversarial Example Detection for DNN Models: A Review and Experimental Comparison
In this paper, we focus on image classification task and attempt to provide a survey for detection methods of test-time evasion attacks on neural network classifiers.
Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification
However, all the works proposed in the literature for generating adversarial examples have used the $L_{p}$ norms ($L_{0}$, $L_{2}$ and $L_{\infty}$) as distance metrics to quantify the similarity between the original image and the adversarial example.
