32 citations · 68 across the 8 of their papers we have counts for
8 papers · 1 filter
Localization of Just Noticeable Difference for Image Compression
Guangan Chen, Hanhe Lin, Oliver Wiedemann +1
The just noticeable difference (JND) is the minimal difference between stimuli that can be detected by a person. The picture-wise just noticeable difference (PJND) for a given refe…
Critical analysis on the reproducibility of visual quality assessment using deep features
Franz Götz-Hahn, Vlad Hosu, Dietmar Saupe
Data used to train supervised machine learning models are commonly split into independent training, validation, and test sets. This paper illustrates that complex data leakage case…
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification
Hui Men, Vlad Hosu, Hanhe Lin +2
Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predi…
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
Vlad Hosu, Hanhe Lin, Tamas Sziranyi +1
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generatin…
Algorithm Selection for Image Quality Assessment
Markus Wagner, Hanhe Lin, Shujun Li +1
Subjective perceptual image quality can be assessed in lab studies by human observers. Objective image quality assessment (IQA) refers to algorithms for estimation of the mean subj…
Effective Aesthetics Prediction with Multi-level Spatially Pooled Features
Vlad Hosu, Bastian Goldlucke, Dietmar Saupe
We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently l…