Combination of SSIM and JND with Content-Transition Classification for Image Quality Assessment
Date Issued
2014
Date
2014
Author(s)
Hsu, Ming-Chung
Abstract
Image quality assessment (IQA) is a crucial feature of many image processing algorithms. The state-of-the-art IQA index, the structural similarity (SSIM) index, has been able to accurately predict image quality by assuming that the human visual system (HVS) separates structural information from nonstructural information in a scene. However, the precision of SSIM is relatively lacking when used to access blurred images. This paper proposes a novel metric of image quality assessment, the JND-SSIM, which adopts the just-noticeable di erence (JND) algorithm to di erentiate between plain, edge, and texture blocks and obtain a visibility threshold map. Based on varying block transition types between the reference and distorted image, SSIM values are assigned respective weights and scaled down by visibility threshold map. We then test our algorithm on the LIVE and TID Image Quality Database, thereby demonstrating that our improved IQA index is much closer to human opinion.
In this work, we integrate our IQA tool into the video conferencing system, and regardless of video conferencing systems are designed by software or hardware encoder architecture. We modi fied the bitrate control flow of
the video conferencing system and the bitrate control module will based on our IQA value to decide bitrate setting. The experimental results show signi cant bitrate savings compared with existing video conferencing system at the same subjective quality.
Subjects
影像品質評量
結構相似性指標
人類視覺系統
最小可察覺差異
SDGs
Type
thesis
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