Binary text image file preprocessing to account for printer dot gain

Zhang, Lu; Veis, Alex; Ulichney, Robert; Allebach, Jan P.

doi:10.1109/icip.2014.7025534

Cited by 2 publications

(2 citation statements)

References 19 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since there is no reference image, we evaluate the image quality with nonreference metrics. Using the non-reference metric specifically define for binary document images in [38], we demonstrate that the visual quality of our restored image has been improved by 5.1%. We zoomed in to sample areas in the test image for better visualization, as shown in Fig.…”

Section: Methodsmentioning

confidence: 93%

Model-Based Iterative Restoration for Binary Document Image Compression with Dictionary Learning

Guo

Lu²,

Allebach

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

The inherent noise in the observed (e.g., scanned) binary document image degrades the image quality and harms the compression ratio through breaking the pattern repentance and adding entropy to the document images. In this paper, we design a cost function in Bayesian framework with dictionary learning. Minimizing our cost function produces a restored image which has better quality than that of the observed noisy image, and a dictionary for representing and encoding the image. After the restoration, we use this dictionary (from the same cost function) to encode the restored image following the symbol-dictionary framework by JBIG2 standard with the lossless mode. Experimental results with a variety of document images demonstrate that our method improves the image quality compared with the observed image, and simultaneously improves the compression ratio. For the test images with synthetic noise, our method reduces the number of flipped pixels by 48.2% and improves the compression ratio by 36.36% as compared with the best encoding methods. For the test images with real noise, our method visually improves the image quality, and outperforms the cutting-edge method by 28.27% in terms of the compression ratio.

show abstract

Section: Methodsmentioning

confidence: 93%

Model-Based Iterative Restoration for Binary Document Image Compression with Dictionary Learning

Guo

Lu²,

Allebach

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

View full text Add to dashboard Cite

show abstract

“…Therefore, halftone-based approaches can be used to collaborate with CMM-based methods. Many halftone-based methods are proposed to process text-based content [27]- [29], and image-based content. For image-based applications, the halftone-based methods can be classified into three types: Amplitude Modulation (AM) [30], Frequency Modulation (FM) [31], and Hybrid Modulation [32].…”

Section: Halftone-based Methodsmentioning

confidence: 99%

A Green and Practical Color Photograph Printing Technology Based on Color Difference Model and Human Perception

Lin

2022

IEEE Access

View full text Add to dashboard Cite

As all kinds of self-contained display products have become popular, various technologies for improving printing applications have gradually been ignored or even stagnated. However, the number of printed matters in life has not decreased. Every day our lives are full of various printed materials, such as newspapers, magazines, leaflets, product packaging, and so on. The waste and pollution caused by printing technology are usually recessive, and not everyone values it. Unfortunately, people often overlook the waste of resources and environmental pollution caused by the manufacturing process, ink, toner, and printers. The method proposed in this paper uses a color difference model to find a manner that is not easy to notice by the human eye to save ink. The proposed method considers feasibility and practicability, and users can quickly find suitable settings for their applications. Also, through the actual printed pictures, the experimental results show that only 67.57% of the original ink consumption is required to obtain an equivalent print quality that is difficult to perceive by the human eye. Our proposed method will benefit the printing industry and reduce ink consumption on consumer printers, contributing to cost control and being nature-friendly.INDEX TERMS Image processing, image quality, image color analysis, green products.

show abstract

Binary text image file preprocessing to account for printer dot gain

Cited by 2 publications

References 19 publications

Model-Based Iterative Restoration for Binary Document Image Compression with Dictionary Learning

Model-Based Iterative Restoration for Binary Document Image Compression with Dictionary Learning

A Green and Practical Color Photograph Printing Technology Based on Color Difference Model and Human Perception

Contact Info

Product

Resources

About