Overview of JPEG AIC
The purpose of the Advanced Image Coding and Evaluations work is to locate and evaluate new scientific developments and advancements in image coding research. Relevant topics include new compression methodologies and quality evaluation methodologies and procedures.
The work of the JPEG AIC project produced a technical report, Guidelines for image coding system evaluation in ISO/IEC TR 29170-1:2017 and a standard, the Evaluation procedure for nearly lossless coding, in ISO/IEC 29170-2:2015. These documents represent best practices adopted and recommended by the JPEG committee, moreover, the procedures embody both objective scoring and subjective assessment that assure a codec’s quality assessment has been developed and tested under the most rigorous conditions expected in a product’s worldwide deployment.
At this time the AIC project is investigating further techniques to improve assessment of different technologies deployed at JPEG. The PLENO project, in particular, has many applications that face challenges to the viewing environment. We welcome member contributions to further this area of our research that should be deployed in a new part of the ISO/IEC 29170 standard family in the near future. These activities are a work-in-progress that continuously seeks to accommodate new coding architectures and evaluation methodologies.
Part 1: Guidelines for image coding system evaluation
This report recommends best practices for coding system evaluation of images and image sequences. ISO/IEC TR 29170-1:2017 defines a common vocabulary of terms for coding system evaluation and divides evaluation methods into three broad categories: subjective assessment, objective assessment and computational assessment.
Part 2: Evaluation procedure for nearly lossless coding
This part normalizes evaluation and grading of a light coding system used for displays and display systems, but is independent of the display technology. This procedure measures whether an observer can distinguish between an uncompressed reference and the reconstructed image to a pre-determined, statistically meaningful level.