English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep-learning based techniques have contributed to the remarkable progress inthe field of automatic image quality assessment (IQA). Existing IQA methods aredesigned to measure the quality of an image in terms of Mean Opinion Score(MOS) at the image-level (i.e. the whole image) or at the patch-level (dividingthe image into multiple units and measuring quality of each patch). Someapplications may require assessing the quality at the pixel-level (i.e. MOSvalue for each pixel), however, this is not possible in case of existingtechniques as the spatial information is lost owing to their networkstructures. This paper proposes an IQA algorithm that can measure the MOS atthe pixel-level, in addition to the image-level MOS. The proposed algorithmconsists of three core parts, namely: i) Local IQA; ii) Region of Interest(ROI) prediction; iii) High-level feature embedding. The Local IQA part outputsthe MOS at the pixel-level, or pixel-by-pixel MOS - we term it 'pMOS'. The ROIprediction part outputs weights that characterize the relative importance ofregion when calculating the image-level IQA. The high-level feature embeddingpart extracts high-level image features which are then embedded into the LocalIQA part. In other words, the proposed algorithm yields three outputs: the pMOSwhich represents MOS for each pixel, the weights from the ROI indicating therelative importance of region, and finally the image-level MOS that is obtainedby the weighted sum of pMOS and ROI values. The image-level MOS thus obtainedby utilizing pMOS and ROI weights shows superior performance compared to theexisting popular IQA techniques. In addition, visualization results indicatethat predicted pMOS and ROI outputs are reasonably aligned with the generalprinciples of the human visual system (HVS).

Pixel-by-pixel Mean Opinion Score (pMOS) for No-Reference Image Quality Assessment