Todd Richard Goodall

Tasking on Natural Statistics of Infrared Images

Natural scene statistics (NSSs) provide powerful, perceptually relevant tools that have been successfully used for image quality analysis of visible light images. Since NSS capture statistical regularities that arise from the physical world, they are relevant to long wave infrared (LWIR) images, which differ from visible light images mainly by the wavelengths captured at the imaging sensors. We show that NSS models of bandpass LWIR images are similar to those of visible light images, but with different parameterizations. Using this difference, we exploit the power of NSS to successfully distinguish between LWIR images and visible light images. In addition, we study distortions unique to LWIR and find directional models useful for detecting the halo effect, simple bandpass models useful for detecting hotspots, and combinations of these models useful for measuring the degree of non-uniformity present in many LWIR images. For local distortion identification and measurement, we also describe a method for generating distortion maps using NSS features. To facilitate our evaluation, we analyze the NSS of LWIR images under pristine and distorted conditions, using four databases, each captured with a different IR camera. Predicting human performance for assessing distortion and quality in LWIR images is critical for task efficacy. We find that NSS features improve human targeting task performance prediction. Furthermore, we conducted a human study on the perceptual quality of noise-and blur-distorted LWIR images and create a new blind image quality predictor for IR images.

Sampled efficient full-reference image quality assessment models

Existing Ml-reference image quality assessment models first compute a full image quality-predictive feature map followed by a spatial pooling scheme, thereby producing a single quality score. Here we study spatial sampling strategies that can be used to more efficiently compute reliable picture quality scores. We develop a random sampling scheme on single scale full-reference image quality assessment models. Based on a thorough analysis of how this random sampling strategy affects the correlations of the resulting pooled scores against human subjective quality judgements, a highly efficient grid sampling scheme is proposed which replaces the ubiquitous convolution operations with local block-based multiplications. Experiments on four different databases show that this block-based sampling strategy can yield results similar to methods that use a complete image feature map, even when the number of feature samples is reduced by 90%.

Blind Picture Upscaling Ratio Prediction

Natural scene statistics are well studied in the context of picture quality assessment and have been used in a wide variety of top-performing picture quality prediction models. Upscaling artifacts have been measured with regards to quality impairment using these kinds of models. However, the assessment and classification of subtle, less discriminable upscaling artifacts remains an unsolved problem. The nearly imperceptible artifacts pertaining to the extent and type of upscaling have not been predicted using natural scene statistics (NSS)-based models. We develop an accurate model for predicting the upscaling ratio applied to any natural image. By decomposing an input image frame using an orthogonal filter bank and locally normalizing the resulting responses, we show that the local energy terms can be used to predict the upscaling ratio. In fact, a simple linear regressor can be trained on these energy measurements; hence, no hyperparameter tuning is necessary. We compare the proposed model with other no-reference models using real-world data contained in the Netflix collection.

Non-uniformity correction of IR images using natural scene statistics

Infrared images are commonly afflicted by distortions such as non-uniformity. Non-uniformity is characterized by horizontal and vertical fixed pattern noise. Accurately estimating the amount of non-uniformity present in an image and removing that amount of non-uniformity noise are open problems. Several estimators of non-uniformity exist, but their ability to estimate degrades with the presence of other sources of noise. Specifically, most of these metrics lack the robustness demanded by a more complete non-uniformity model. Previous non-uniformity correction algorithms are compared and found to underperform relative to a more complete model of non-uniformity that we have developed. Using this model, we have created a new denoising algorithm, which we call the Gaussian scale mixture perceptual pattern denoiser. The new model and algorithm can fully characterize non-uniformity using covariance matrices.