Clustering-based quantisation for PDE-based image compression

Abstract

Finding optimal data for inpainting is a key problem in the context of partial differential equation based image compression. The data that yields the most accurate reconstruction is real-valued. Thus, quantisation models are mandatory to allow an efficient encoding. These can also be understood as challenging data clustering problems. Although clustering approaches are well suited for this kind of compression codecs, very few works actually consider them. Each pixel has a global impact on the reconstruction and optimal data locations are strongly correlated with their corresponding colour values. These facts make it hard to predict which feature works best.In this paper we discuss quantisation strategies based on popular methods such as k-means. We are lead to the central question which kind of feature vectors are best suited for image compression. To this end we consider choices such as the pixel values, the histogram or the colour map.Our findings show that the number of colours can be reduced significantly without impacting the reconstruction quality. Surprisingly, these benefits do not directly translate to a good image compression performance. The gains in the compression ratio are lost due to increased storage costs. This suggests that it is integral to evaluate the clustering on both, the reconstruction error and the final file size.

Improved Exemplar based Image Inpainting for Partial Instance Occlusion Handling with K-means Clustering and YCbCr Color Space

“…'Partial Differential Equation (PDE)' is based on an iterative approach [3,14,29], produces good results when missed regions are smaller and time consumption is more when missed regions are larger.…”

“…This novel amended approach of K-means with exemplar based on the inpainting method has shown a tradeoff in subjective perception and execution time. In literature, quantization with diffusion based inpainting method [3,8,18] and PDE based method [3][4]14] has been attempted. The K-means approach has been used here for the color image quantization.…”

Quantisation Scale-Spaces

“…In the present paper, we show that quantisation methods can also imply scale-spaces and that this leads to practical consequences for inpainting-based compression. In this application context, quantisation has so far only been the main focus in the work of Hoeltgen et al [7] who compared multiple different strategies. However, they did not consider scale-space ideas and concluded that non-uniform quantisation does not offer advantages over uniform ones in inpainting-based compression.…”

Towards PDE-Based Video Compression with Optimal Masks Prolongated by Optic Flow

“…Transform-based image and video compression algorithms are still the preferred choice in many applications [33]. However, in recent years there has been a growing interest in alternative approaches [1,11,18,30]. It has been shown that partial differential equation (PDE)-based methods represent a viable alternative in the context of image compression.…”