Given the lack of accessible infrared compressed images’ benchmarks annotated by human subjects, this work presents a new database with the aim of studying both subjective and objective image quality assessment (IQA) on compressed long wavelength infrared (LWIR) images. The database contains 20 reference (pristine) images and 200 distorted (degraded) images obtained by application of the most known compression algorithms used in multimedia and communication fields, namely: JPEG and JPEG-2000. Each compressed image is evaluated by 31 subjects having different levels of experience in LWIR images. Mean opinion scores (MOS) and natural scene statistics (NSS) of pristine and compressed images are elaborated to study the performance of the database. Five analyses are conducted on collected images and subjective scores, namely: analysis by compression type, analysis by file size, analysis by reference image, analysis by quality level and analysis by subject. Moreover, a wide set of objective IQA metrics is applied on the images and the obtained scores are compared with the collected subjective scores. Results show that objective IQA measures correlate with human subjective results with a degree of agreement up to 95 %, so this benchmark is promising to improve existing and develop new IQA measures for compressed LWIR images. Thanks to a real-world surveillance original images based on which we analyze how image compression and quality level affect the quality of compressed images, this database is primarily suitable for (military and civilian) surveillance applications. The database is accessible via the link: https://github.com/azedomar/compressed-LWIR-images-IQA-database. As a follow-up to this work, an extension of the database is underway to study other types of distortion in addition to compression.

The evaluation of disparity (range) maps includes the selection of an objective image quality (or error) measure. Among existing measures, the percentage of bad matched pixels is commonly used. However, it requires a disparity error tolerance and ignores the relationship between range and disparity. In this research, twelve error measures are characterized in order to provide the bases to select accurate stereo algorithms during the evaluation process. Adaptations of objective quality measures for disparity maps’ accuracy evaluation are proposed. The adapted objective measures operate in a manner similar to the original objective measures, but allow special handling of missing data. Additionally, the adapted objective measures are sensitive to errors in range and surface structure, which cannot be measured using the bad matched pixels. Their utility was demonstrated by evaluating a set of 50 stereo disparity algorithms known in the literature. Consistency evaluation of the proposed measures was performed using the two conceptually different stereo algorithm evaluation methodologies—ordinary ranking and partition and grouping of the algorithms with comparable accuracy. The evaluation results showed that partition and grouping make a fair judgment about disparity algorithms’ accuracy.

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