No-Reference Quality Assessment for Pansharpened Images via Opinion-Unaware Learning

Zhou, Bingzhong; Shao, Feng; Meng, Xiangchao; Fu, Randi; Ho, Yo‐Sung

doi:10.1109/access.2019.2905615

Cited by 16 publications

(7 citation statements)

References 64 publications

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“…These NR-IQA methods are either based on statistical features or deep features. Some researchers present NR-IQA methods based on statistical features for pan-sharpening images [39][40][41]. Deep features are used in some NR-IQA methods for pan-sharpening images [42,43].…”

Section: No-reference Image Quality Assessmentmentioning

confidence: 99%

No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning

Li,

Dong,

et al. 2024

Remote Sensing

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In hyperspectral image (HSI) reconstruction tasks, due to the lack of ground truth in real imaging processes, models are usually trained and validated on simulation datasets and then tested on real measurements captured by real HSI imaging systems. However, due to the gap between the simulation imaging process and the real imaging process, the best model validated on the simulation dataset may fail on real measurements. To obtain the best model for the real-world task, it is crucial to design a suitable no-reference HSI quality assessment metric to reflect the reconstruction performance of different models. In this paper, we propose a novel no-reference HSI quality assessment metric via ranking feature learning (R-NHSIQA), which calculates the Wasserstein distance between the distribution of the deep features of the reconstructed HSIs and the benchmark distribution. Additionally, by introducing the spectral self-attention mechanism, we propose a Spectral Transformer (S-Transformer) to extract the spatial-spectral representative deep features of HSIs. Furthermore, to extract quality-sensitive deep features, we use quality ranking as a pre-training task to enhance the representation capability of the S-Transformer. Finally, we introduce the Wasserstein distance to measure the distance between the distribution of the deep features and the benchmark distribution, improving the assessment capacity of our method, even with non-overlapping distributions. The experimental results demonstrate that the proposed metric yields consistent results with multiple full-reference image quality assessment (FR-IQA) metrics, validating the idea that the proposed metric can serve as a substitute for FR-IQA metrics in real-world tasks.

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Section: No-reference Image Quality Assessmentmentioning

confidence: 99%

No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning

Li,

Dong,

et al. 2024

Remote Sensing

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“…They assign more weight to scores from those patches over others during score aggregation [17]. (IL)NIQE features can also reliably predict quality of multi-spectral images [18]. Few methods even tread the boundaries of opinion (un-)awareness and/or (no-)reference [11,6].…”

Section: Background and Related Workmentioning

confidence: 99%

Potential of Deep Features for Opinion-Unaware, Distortion-Unaware, No-Reference Image Quality Assessment

Mukherjee

Valenzise

Cheng

2020

Lecture Notes in Computer Science

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Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted "reference" version of the input image to compare with, in order to predict this score. However, recent "No-reference" methods circumvent this requirement by modelling the distribution of clean image features, thereby making them more suitable for practical use. However, majority of such methods either use hand-crafted features or require training on human opinion scores (supervised learning), which are difficult to obtain and standardise. We explore the possibility of using deep features instead, particularly, the encoded (bottleneck) feature maps of a Convolutional Autoencoder neural network architecture. Also, we do not train the network on subjective scores (unsupervised learning). The primary requirements for an IQA method are monotonic increase in predicted scores with increasing degree of input image distortion, and consistent ranking of images with the same distortion type and content, but different distortion levels. Quantitative experiments using the Pearson, Kendall and Spearman correlation scores on a diverse set of images show that our proposed method meets the above requirements better than the state-of-art method (which uses hand-crafted features) for three types of distortions: blurring, noise and compression artefacts. This demonstrates the potential for future research in this relatively unexplored sub-area within IQA.

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“…This framework combines the original image, its histogram equalised product, and its visually pleasing version created by a sigmoid transfer function. Zhou et al [24] proposed an NR‐IQA method to blindly predict the quality of pansharpened images via opinion‐unaware learning. For image sharpness, Gu et al [25] proposed a blind sharpness metric in the autoregressive (AR) parameter which is established via the analysis of AR model parameters space.…”

Section: Related Workmentioning

confidence: 99%

Enhanced image no‐reference quality assessment based on colour space distribution

Zhang

et al. 2020

IET Image Processing

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No-Reference Quality Assessment for Pansharpened Images via Opinion-Unaware Learning

Cited by 16 publications

References 64 publications

No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning

No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning

Potential of Deep Features for Opinion-Unaware, Distortion-Unaware, No-Reference Image Quality Assessment

Enhanced image no‐reference quality assessment based on colour space distribution

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