Introduction

Blocking artifact is the most obvious artifact associated with JPEG (or other block-based coding), especially at very low bit rate. Some post-processing methods have been proposed to reduce this annoy artifact by smoothing, which, unfortunately, will incur blurring artifact. Different implementations may use different strategy to balance the trade off. To compare the performance of different implementations, we need to find a way to evaluate the distortion in processed image.
Subjective tests are the most reliable but they are too time-consuming for wide application. Although criticized for not taking into account of the property of human vision system (HVS), MSE and its inherences (PSNR or so) are still the dominant objective image quality metrics in the field. Many other objective image quality metrics, which imitate more or less of HVS, have been proposed. As we know, there is still no such a “magic” objective image quality metrics that can solve most of the problems. Most successful metrics are targeted at a specific (or limited) application filed. Since blocking and blurring are the most common artifacts in JPEG, it will be very helpful if we can find a better objective distortion metrics that is more closely related to our subjective impression of blocking and blurring artifacts.

In this project, I
1) Implement several image quality metrics: RMSE, Liu et al’s BMR(blocking-to-masking ratio) and Eskicioglu’s EOBD
2) Evaluate them through a simple but very efficient subjective test.
3) Propose a new metrics, which is a combination of pixel-based Root-Mean-Square-Error (RMSE) and block-based distortion metrics. This metrics proves superior to others.