Blind Image Quality Assessment Based on Geometric Order Learning
A novel approach to blind image quality assessment called quality comparison network (QCN) is proposed in this paper which sorts the feature vectors of input images according to their quality scores in an embedding space. QCN employs comparison transformers (CTs) and score pivots which act as the centroids of feature vectors of similar-quality images. Each CT updates the score pivots and the feature vectors of input images based on their ordered correlation. To this end we adopt four loss functions. Then we estimate the quality score of a test image by searching the nearest score pivot to its feature vector in the embedding space. Extensive experiments show that the proposed QCN algorithm yields excellent image quality assessment performances on various datasets. Furthermore QCN achieves great performances in cross-dataset evaluation demonstrating its superb generalization capability. The source codes are available at https://github.com/nhshin-mcl/QCN.
Code (1)
Tasks
Image Quality AssessmentNo-Reference Image Quality AssessmentSimilar Papers 제목 키워드 기반
Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention
BIQA (Blind Image Quality Assessment) is an important field of study that evaluates images automatically. Although significant progress has been made, blind image quality assessment remains a difficult task since images …
Blind Image Quality AssessmentImage Quality AssessmentRL-ScanIQA: Reinforcement-Learned Scanpaths for Blind 360°Image Quality Assessment
Blind 360°image quality assessment (IQA) aims to predict perceptual quality for panoramic images without a pristine reference. Unlike conventional planar images, 360°content in immersive environments restricts viewers to…
Image Quality AssessmentDeep Superpixel-based Network for Blind Image Quality Assessment
The goal in a blind image quality assessment (BIQA) model is to simulate the process of evaluating images by human eyes and accurately assess the quality of the image. Although many approaches effectively identify degrad…
Image Quality AssessmentNo-Reference Image Quality AssessmentSpatial Moment Pooling Improves Neural Image Assessment
In recent years, there has been widespread attention drawn to convolutional neural network (CNN) based blind image quality assessment (IQA). A large number of works start by extracting deep features from CNN. Then, those…
Blind Image Quality AssessmentImage Quality AssessmentContrastive Order Learning: A General Framework for Ordinal Regression
We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning. While contrastive learning effectively lev…
Image Quality AssessmentVideo Quality AssessmentContrastive LearningAge Estimation