This paper presents secondary Standard Quality Scale (SQS2) rankings in overall quality JNDs for a subjective analysis of the 3 axes of noise, amplitude, spectral content, and noise type, based on the ISO 20462 softcopy ruler protocol. For the initial pilot study, a Python noise simulation model was created to generate the matrix of noise masks for the softcopy ruler base images with different levels of noise, different low pass filter noise bandwidths and different band pass filter center frequencies, and 3 different types of noise: luma only, chroma only, and luma and chroma combined. Based on the lessons learned, the full subjective experiment, involving 27 observers from Google, NVIDIA and STMicroelectronics was modified to incorporate a wider set of base image scenes, and the removal of band pass filtered noise masks to ease observer fatigue. Good correlation was observed with the Aptina subjective noise study. The absence of tone mapping in the noise simulation model visibly reduced the contrast at high levels of noise, due to the clipping of the high levels of noise near black and white. Under the 34-inch viewing distance, no significant difference was found between the luma only noise masks and the combined luma and chroma noise masks. This was not the intuitive expectation. Two of the base images with large uniform areas, ‘restaurant’ and ‘no parking’, were found to be consistently more sensitive to noise than the texture rich scenes. Two key conclusions are (1) there are fundamentally different sensitivities to noise on a flat patch versus noise in real images and (2) magnification of an image accentuates visual noise in a way that is non-representative of typical noise reduction algorithms generating the same output frequency. Analysis of our experimental noise masks applied to a synthetic Macbeth ColorChecker Chart confirmed the color-dependent nature of the visibility of luma and chroma noise.
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.
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.