Improving Multiscale Recurrent Pattern Image Coding with Deblocking Filtering

Abstract

The Multidimensional Multiscale Parser (MMP) algorithm is an image encoder that approximates the image blocks by using recurrent patterns, from an adaptive dictionary, at different scales. This encoder performs well for a large range of image data. However, images encoded with MMP suffer from blocking artifacts. This paper presents the design of a deblocking filter that improves the performance the MMP. We present the results of our research, that aims to increase the performance of MMP, particularly for smooth images, without causing quality losses for other image types, where its performance is already up to 5 dB better than that of top transform based encoders. For smooth images, the proposed filter introduces relevant perceptual quality gains by efficiently eliminating the blocking effects, without introducing the usual blurring artifacts. Besides this, we show that, unlike traditional deblocking algorithms, the proposed method also improves the objective quality of the decoded image, achieving PSNR gains of up to about 0.3 dB. With such gains, MMP reaches an almost equivalent performance to that of the state-of-the-art image encoders (equal to that of JPEG2000 for higher compression ratios), for smooth images, while maintaining its gains for non-smooth images. In fact, for all image types, the proposed method provides significant perceptual improvements, without sacrificing the PSNR performance.