Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media.
In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms.
We propose a new manifold learning method, Locally Linear Image Structural Embedding (LLISE), and kernel LLISE for learning this manifold.
Despite the advances of deep learning in specific tasks using images, the principled assessment of image fidelity and similarity is still a critical ability to develop.
We compared 8 state-of-the-art algorithms for blind IQA and showed that an oracle, able to predict the best performing method for any given input image, yields a hybrid method that could outperform even the best single existing method by a large margin.
In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency.
In this paper, we propose a noise-aware exposure control algorithm for robust robot vision.
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions.
Significant progress has been made in the past decade for full-reference image quality assessment (FR-IQA).
As a result, models trained in a well-controlled laboratory environment with synthetic distortions fail to generalize to realistic distortions, whose data distribution is different.