We review the design of the SSIM quality metric and offer an alternative model of SSIM computation, utilizing subband decomposition and identical distance measures in each subband. We show that this model performs very close to the original and offers many advantages from a methodological standpoint. It immediately brings several possible explanations of why SSIM is effective. It also suggests a simple strategy for band noise allocation optimizing SSIM scores. This strategy may aid the design of encoders or pre-processing filters for video coding. Finally, this model leads to more straightforward mathematical connections between SSIM, MSE, and SNR metrics, improving previously known results.
Contrast sensitivity functions (CSFs) describe the smallest visible contrast across a range of stimulus and viewing parameters. CSFs are useful for imaging and video applications, as contrast thresholds describe the maximum of color reproduction error that is invisible to the human observer. However, existing CSFs are limited. First, they are typically only defined for achromatic contrast. Second, even when they are defined for chromatic contrast, the thresholds are described along the cardinal dimensions of linear opponent color spaces, and therefore are difficult to relate to the dimensions of more commonly used color spaces, such as sRGB or CIE L*a*b*. Here, we adapt a recently proposed CSF to what we call color threshold functions (CTFs), which describe thresholds for color differences in more commonly used color spaces. We include color spaces with standard dynamic range gamut (sRGB, YCbCr, CIE L*a*b*, CIE L*u*v*) and high dynamic range gamut (PQ-RGB, PQ-YCbCr and ICTCP). Using CTFs, we analyze these color spaces in terms of coding efficiency and contrast threshold uniformity.
Visual noise is a metric for measuring the amount of noise perception in images taking into account the properties of the human visual system (HVS). A visual noise measurement method is specified in Annex B of ISO 15739, which has been used as a useful measurement metric over the years since its introduction. As several issues have been questioned recently, it is now being investigated for revision involving changes to the CSF, color space used for rms noise value measurements, and visual noise formula combining rms values in three color channels. To derive visual noise formula involving color noise weighting coefficients representing HVS sensitivity to noise, subjective experiments using simulated luminance and color noise images were done. The improvement of calculation stability and HVS correspondence was verified using simulation and real camera images under various conditions. Finally, subjective experiments using real camera images were performed to validate the revised method and update the color noise coefficients considering practical measurement cases.
With the publication of the second edition of the ISO 15739 Standard [1] in 2013 the measurement of “visual noise” became a normative part of the standard. Over the years the algorithm has proven to be useful and reliable for the judgement of the visibility of noise in images captured by digital cameras. Nevertheless a few aspects of the measurement procedures were questioned by some experts like e.g. the relation of the contrast sensitivity function (csf) for the luminance and the two chrominance channels. And the resulting weighting factors for the three channels also depend on the csf relation. In addition, some experts would like to use the more common CIELAB space instead of CIELUV. For these reasons the responsible ISO technical committee 42 working group 18 is looking into a revision of the visual noise section of the standard. This paper describes the procedure the group is undertaking to solve the remaining issues in the upcoming revision.
Imaging system performance measures and Image Quality Metrics (IQM) are reviewed from a systems engineering perspective, focusing on spatial quality of still image capture systems. We classify IQMs broadly as: Computational IQMs (CP-IQM), Multivariate Formalism IQMs (MF-IQM), Image Fidelity Metrics (IF-IQM), and Signal Transfer Visual IQMs (STV-IQM). Comparison of each genre finds STV-IQMs well suited for capture system quality evaluation: they incorporate performance measures relevant to optical systems design, such as Modulation Transfer Function (MTF) and Noise-Power Spectrum (NPS); their bottom-up, modular approach enables system components to be optimized separately. We suggest that correlation between STV-IQMs and observer quality scores is limited by three factors: current MTF and NPS measures do not characterize scene-dependent performance introduced by imaging system non-linearities; contrast sensitivity models employed do not account for contextual masking effects; cognitive factors are not considered. We hypothesize that implementation of scene and process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures should mitigate errors originating from scene dependent system performance. Further, we propose implementation of contextual contrast detection and discrimination models to better represent low-level visual performance in image quality analysis. Finally, we discuss image quality optimization functions that may potentially close the gap between contrast detection/discrimination and quality.