Blind Image Quality Assessment
21 papers with code • 0 benchmarks • 0 datasets
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We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions.
Recognizing this, we propose a new representation of perceptual image quality, called probabilistic quality representation (PQR), to describe the image subjective score distribution, whereby a more robust loss function can be employed to train a deep BIQA model.
The proposed method, SFA, is compared with nine representative blur-specific NR-IQA methods, two general-purpose NR-IQA methods, and two extra full-reference IQA methods on Gaussian blur images (with and without Gaussian noise/JPEG compression) and realistic blur images from multiple databases, including LIVE, TID2008, TID2013, MLIVE1, MLIVE2, BID, and CLIVE.
To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably.
So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model.
Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically distorted images.
Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets.
The benchmark LIVE 3D phase-I, phase-II, and IRCCyN/IVC 3D databases have been used to evaluate the performance of the proposed approach.