EE 638: Principles of Digital Color Imaging Systems Lecture xx: Image Quality

Image quality – an illustrative example Prof. Charles A. Bouman is honored for his service as Editor of the IEEE Transactions on Image Processing (shown with Prof. Ali H. Sayed, UCLA, VP-Conferences and Prof. Jose M. F. Moura, CMU, President, IEEE SP Society) Open image in Photoshop to be sure about what we are seeing. What are the image quality defects that are visible here? ”It's a little frightening to think that this picture is associated with the Transactions on Image Processing” – C. A. Bouman

Image quality defects present in image Noise in low-light parts of scene Moire on Prof. Bouman’s shirt JPEG artifacts Poor color rendering – too red Poor contrast and tone – too dark Glare from flash

Overview As imaging continues to become more pervasive, image quality has become a topic of growing interest. We have done a great deal of work in this area over the years within our laboratory. I will use this work as a partial basis for the discussion in class. These will be augmented by materials from other sources.

Image quality resources – textbooks Brian W. Keelan, Handbook of Image Quality Characterization and Prediction, Marcel Dekker, 2002 (Kodak’s “go-to guy” for image quality). Peter Engeldrum, Psychometric Scaling: A Toolkit for Imaging Systems Development, Imcotek Press, 2000.

Image quality resources – journals IEEE Transactions on Image Processing Journal of Electronic Imaging (IS&T and SPIE) Journal of Imaging Science and Technology (IS&T)

Image quality resources – conferences IEEE International Conference on Image Processing (ICIP) IS&T/SPIE Electronic Imaging Image Quality and System Performance Human Vision and Electronic Imaging Color Imaging: Displaying, Processing, Hardcopy, and Applications Digital Photography Others… IS&T International Conference on Digital Printing Technologies (NIP) (The conference has shifted focus to functional printing.) IS&T Color Imaging Conference

Image quality resources – standards ISO/IEC 13660 Information technology – Office equipment – Measurement of image quality attributes for hardcopy output – Binary monochrome text and graphic images. (http://www.iso.org/iso/catalogue_detail.htm?csnumber=22145)

Image quality – role of human viewer Image quality is all about what humans see Pyschophysics is therefore an essential tool for understanding image quality

Three image quality questions (refer to photo of Prof. Bouman) Is image quality impairment visible? Is image quality impairment acceptable? Among different choices for a particular component of the imaging system that impact this particular image quality impairment, which do I prefer? The answer to Question 1 depends on viewing conditions. The answer to Questions 2 and 3 depends on intended application, market segment, and viewer characteristics.

Imaging Pipeline Image output Image processing Image capture Enhance Compose Compress Camera scanner Display Printer

Image quality perspectives – image vs. system Imaging systems based Resolution (modulation transfer function) Dynamic range Noise characteristics Image-based Sharpness Contrast Graininess/mottle

Image quality vs. print quality Broader viewpoint Often focuses on issues that arise during Image processing phase below, especially compression. May also consider image capture and display Print quality Specifically considers issues that arise during printing Image capture Image processing Image output

Typical image quality issues See discussion of photo of Prof. Bouman

Typical print quality issues Bands – orthogonal to process direction Streaks – parallel to process direction Spots Repetitive Random Color plane registration errors Ghosting Toner scatter Swath misalignment http://www.hp.com/cpso-support-new/pq/4700/home.html

Image quality assessment functionalities Metrics vs. maps Local or global strength of a particular defect – a single number Map showing defect strength throughout the image – an image Single defect vs. summative measures Assess strength of a single defect, i.e. noise Assess overall image quality – must account for all significant defects and their interactions Reference vs. no-reference methods

Image quality assessment factors Masking – image content may mask visibility of defect Texture Edges Tent-pole effect – worse defect dominates percept of image quality defects and overall assessment of image quality

General measures for imaging systems quality Based on use of specific input targets Point-and-shoot camera – Walmart photoprocessor – desktop scanner imaging pipeline (Sangho Kim and Buyue Zhang) Modulation transfer function (MTF) System noise (primarily film grain noise) Application – optimal unsharp mask and adaptive bilateral filter Inkjet printer (Woon-Young Jang) Complicated by unique characteristics of halftoning process Application – pre-enhancement of digital photographs before printing to compensate for roll-off in printer MTF

General measures for image quality Sharpness metrics and pyschophysical assessment (Buyue Zhang) No-reference method Image fidelity assessor (Chris Taylor and Wen Wu) Reference-based Explicitly models processing of HVS Produces visible differences map Application – no specific target application Formatter qualification (Du-Yong Ng) Targeted toward certain classes of artifacts, but generally applicable Application – firmware qualification for printer development

Measuring imaging systems artifacts – banding Comparison between different printers (10-printer experiment) (Yousun Bang) Softcopy environment for monochrome banding assessment (Osman Arslan) Absolute threshold for banding Tool for banding analysis (Thanh Ha) Softcopy environment for color banding assessment (Byungseok Min) Discrimination threshold for color banding Absolute threshold for color banding Relative roles of lightness, hue, and saturation in banding visibility

Measuring imaging systems artifacts – quality of lines and edges Toner scatter (Hyung Jun Park) Inkjet swath misalignment (Edgar Bernal)

Measuring image artifacts JPEG ringing artifacts (Xiaojun Feng and Sirui Hu) Moire artifacts (Mu Qiao) Image rescaling artifacts (Ari Suwendi)