Mr. Jonathan B. Phillips
Staff Image Scientist at Google
SPIE Involvement:
Author | Instructor
Publications (8)

PROCEEDINGS ARTICLE | February 3, 2014
Proc. SPIE. 9016, Image Quality and System Performance XI
KEYWORDS: Signal to noise ratio, Optical filters, Visualization, Electrons, Photography, Interference (communication), Linear filtering, Image quality, Image filtering, RGB color model

PROCEEDINGS ARTICLE | February 4, 2013
Proc. SPIE. 8653, Image Quality and System Performance X
KEYWORDS: Human-machine interfaces, Digital image processing, Visualization, Calibration, Image quality, Digital imaging, Light sources and illumination, Image display, Standards development, Image quality standards

PROCEEDINGS ARTICLE | January 25, 2012
Proc. SPIE. 8293, Image Quality and System Performance IX
KEYWORDS: Signal to noise ratio, Cell phones, Visualization, Imaging systems, Spatial frequencies, Cameras, Image processing, Image quality, Modulation transfer functions, Contrast sensitivity

PROCEEDINGS ARTICLE | February 22, 2010
Proc. SPIE. 7527, Human Vision and Electronic Imaging XV
KEYWORDS: Light sources, Eye, Image compression, Modulation, Reflection, Calibration, LCDs, Bidirectional reflectance transmission function, High dynamic range imaging, Motion analysis

PROCEEDINGS ARTICLE | January 18, 2010
Proc. SPIE. 7529, Image Quality and System Performance VII
KEYWORDS: Signal to noise ratio, Optical filters, Imaging systems, Cameras, Manufacturing, Image quality, Image filtering, Standards development, Image quality standards, Steiner quadruple pulse system

SPIE Journal Paper | January 1, 2010
JEI Vol. 19 Issue 01
KEYWORDS: Image quality, Printing, RGB color model, CMYK color model, Modulation transfer functions, Quality measurement, Statistical analysis, Visualization, Image processing, Profiling

Showing 5 of 8 publications
Course Instructor
SC1049: Benchmarking Image Quality of Still and Video Imaging Systems
Because image quality is multi-faceted, generating a concise and relevant evaluative summary of photographic systems can be challenging. Indeed, benchmarking the image quality of still and video imaging systems requires that the assessor understands not only the capture device itself, but also the imaging applications for the system. This course explains how objective metrics and subjective methodologies are used to benchmark image quality of photographic still image and video capture devices. The course will go through key image quality attributes and the flaws that degrade those attributes, including causes and consequences of the flaws on perceived quality. Content will describe various subjective evaluation methodologies as well as objective measurement methodologies relying on existing standards from ISO, IEEE/CPIQ, ITU and beyond. Because imaging systems are intended for visual purposes, emphasis will be on the value of using objective metrics which are perceptually correlated and generating benchmark data from the combination of objective and subjective metrics. The course "SC1157 Camera Characterization and Camera Models," describing camera models and objective measurements, complements the treatment of perceptual models and subjective measurements provided here.
SC1157: Camera Characterization and Camera Models
Image Quality depends not only on the camera components, but also on lighting, photographer skills, picture content, viewing conditions and to some extent on the viewer. While measuring or predicting a camera's image quality as perceived by users can be an overwhelming task, many camera attributes can be accurately characterized with objective measurement methodologies. This course provides an insight on camera models, examining the mathematical models of the three main components of a camera (optics, sensor and ISP) and their interactions as a system (camera) or subsystem (camera at the raw level). The course describes methodologies to characterize the camera as a system or subsystem (modeled from the individual component mathematical models), including lab equipment, lighting systems, measurements devices, charts, protocols and software algorithms. Attributes to be discussed include exposure, color response, sharpness, shading, chromatic aberrations, noise, dynamic range, exposure time, rolling shutter, focusing system, and image stabilization. The course will also address aspects that specifically affect video capture, such as video stabilization, video codec, and temporal noise. The course "SC1049 Benchmarking Image Quality of Still and Video Imaging Systems," describing perceptual models and subjective measurements, complements the treatment of camera models and objective measurements provided here.
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