CD2 : Combined Distances of Contrast Distributions for the Assessment of Perceptual Quality of Image Processing
The quality of visual input is very important for both human and machine perception. Consequently many processing techniques exist that deal with different distortions. Usually image processing is applied freely and lacks redundancy regarding safety. We propose a novel image comparison method called the Combined Distances of Contrast Distributions (CD2) to protect against errors that arise during processing. Based on the distribution of image contrasts a new reduced-reference image quality assessment (IQA) method is introduced. By combining various distance functions excellent performance on IQA benchmarks is achieved with only a small data and computation overhead.
Code (0)
등록된 구현이 없습니다.
Tasks
Image Quality AssessmentSmall Data Image ClassificationSimilar Papers 제목 키워드 기반
An Investigation of Incorporating Mamba for Speech Enhancement
This work aims to study a scalable state-space model (SSM), Mamba, for the speech enhancement (SE) task. We exploit a Mamba-based regression model to characterize speech signals and build an SE system upon Mamba, termed …
MambaSpeech EnhancementBeyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models
Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.e., In-Distribution (InD), or from OoD. Diffusion Models (DMs) are recent…
Out-of-Distribution DetectionOut of Distribution (OOD) DetectionHepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a crucial role in the detection and characterization of focal liver lesions, with the hepatobiliary phase (HBP) providing essential diagnostic informat…
DenoisingDiagnosticImage GenerationRMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content
With recent advances in deep learning, numerous algorithms have been developed to enhance video quality, reduce visual artifacts, and improve perceptual quality. However, little research has been reported on the quality …
Contrastive LearningVideo EnhancementVideo Quality AssessmentVisual Question Answering (VQA)Understanding and Simplifying Perceptual Distances
Perceptual metrics based on features of deep Convolutional Neural Networks (CNNs) have shown remarkable success when used as loss functions in a range of computer vision problems and significantly outperform classica…
Perceptual Distance