Post-processing of compressed noisy images by BM3D filter

Abstract

Acquired images are often noisy. Since the amount of such images increases, they should be compressed where lossy compression is often applied for several reasons. Such compression is associated with the phenomena of specific image filtering due to lossy compression and the possible existence of an optimal operation point (OOP). However, such filtering is not perfect, and residual noise can be quite intensive even if an image is compressed at the so-called optimal operation point. Then, additional post-filtering can be applied. Thus, the basic subject of this paper is the post-processing of noisy images compressed in a lossy manner. The main goal of this paper is to consider the possible application of a block-matching 3-dimensional (BM3D) filter to images corrupted by additive white Gaussian noise compressed by a better portable graphics (BPG) coder with a compression ratio smaller than that for the optimal operation point and in OOP neighborhood. The tasks of this paper are to analyze the efficiency of compressed image post-processing depending on noise intensity, image complexity, coder compression parameter Q, and filter threshold parameter β according to different quality metrics and to provide practical recommendations on setting the filter and coder parameters. The main result is that the post-processing efficiency decreases when the coder compression parameter increases and becomes negligible for a coder compression parameter slightly larger than its value for OOP. The post-processing efficiency is larger for simpler structure images and larger noise intensity. Compressed image quality due to post-processing improves according to the standard criterion peak signal-to-noise ratio and visual quality metrics. For larger coder compression parameters, the optimal threshold shifts toward smaller values. In conclusion, we demonstrate the efficiency of post-processing and show that the BM3D filter outperforms the standard discrete cosine-based (DCT) filter. We also provide recommendations for filter parameter setting. We also outline possible research directions for the future.

Keywords

• lossy compression
• noisy images
• coders
• quality metrics
• post-filtering