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

Smartphone is the most successful consumer electronic product in today'smobile social network era. The smartphone camera quality and its imagepost-processing capability is the dominant factor that impacts consumer'sbuying decision. However, the quality evaluation of photos taken fromsmartphones remains a labor-intensive work and relies on professionalphotographers and experts. As an extension of the prior CNN-based NR-IQAapproach, we propose a multi-task deep CNN model with scene type detection asan auxiliary task. With the shared model parameters in the convolution layer,the learned feature maps could become more scene-relevant and enhance theperformance. The evaluation result shows improved SROCC performance compared totraditional NR-IQA methods and single task CNN-based models.

Multi-task deep CNN model for no-reference image quality assessment on smartphone camera photos