Adaptive Screen Content Image Enhancement Strategy using Layer-based Segmentation

The ubiquitous screen content images (SCIs) play a significant role in various scenarios currently. However, most SCIs captured by consumer devices are frequently corrupted with distortions, especially contrast distortion. Unlike the natural images, SCIs are composed of text, graphics and natural scene pictures so that traditional image enhancement methods are not suitable for these compound images. Therefore, we innovatively proposed an adaptive strategy for enhancing SCIs in this paper. Firstly, we devised a segmentation method to divide SCI into text and pictorial regions. Next, the famous guided image filter (GIF) with big and small kernel sizes served as unsharpness masking for processing different regions adaptively. For verifying performance, the proposed method was tested on recently prevalent SCI datasets including SIQAD, and Webpage Dataset. Experimental results indicate that the proposed approach outperforms state-of-the-art methods in most SCIs with flat background.

[1]  Xiongkuo Min,et al.  Saliency-induced reduced-reference quality index for natural scene and screen content images , 2018, Signal Process..

[2]  Nenghai Yu,et al.  A Low-Complexity Screen Compression Scheme for Interactive Screen Sharing , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Weisi Lin,et al.  Perceptual Quality Assessment of Screen Content Images , 2015, IEEE Transactions on Image Processing.

[4]  Shervin Minaee,et al.  Screen content image segmentation using least absolute deviation fitting , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[5]  Weisi Lin,et al.  The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement , 2016, IEEE Transactions on Cybernetics.

[6]  Zhou Wang,et al.  Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images , 2017, IEEE Transactions on Image Processing.

[7]  Qi Zhao,et al.  Webpage Saliency , 2014, ECCV.

[8]  Weisi Lin,et al.  A Psychovisual Quality Metric in Free-Energy Principle , 2012, IEEE Transactions on Image Processing.

[9]  Shervin Minaee,et al.  Screen content image segmentation using sparse-smooth decomposition , 2015, 2015 49th Asilomar Conference on Signals, Systems and Computers.

[10]  Pengwei Hao,et al.  Compound image compression for real-time computer screen image transmission , 2005, IEEE Transactions on Image Processing.

[11]  Wenjun Zhang,et al.  Automatic Contrast Enhancement Technology With Saliency Preservation , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Wenjun Zhang,et al.  No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization , 2017, IEEE Transactions on Cybernetics.

[13]  Wen Gao,et al.  Subjective and Objective Quality Assessment of Compressed Screen Content Images , 2016, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[14]  Yücel Altunbasak,et al.  A Histogram Modification Framework and Its Application for Image Contrast Enhancement , 2009, IEEE Transactions on Image Processing.

[15]  Weisi Lin,et al.  A general histogram modification framework for efficient contrast enhancement , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[16]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[17]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.