Lossless image coding using hierarchical decomposition and recursive partitioning

Abstract

State-of-the-art lossless image compression schemes, such as JPEG-LS and CALIC, have been proposed in the context, have been proposed in the context-adaptive predictive coding framework. The essential components of the framework are prediction followed by entropy coding of the resulting residuals. In the framework, an image is encoded in a predefined order, such as, the raster scan order. It relies on the observation that the intensity value of a pixel is highly correlated with its previously encoded neighboring pixels and thus they can be used to make an effective prediction for the current pixel. The beneficial effect of prediction is that the zero-order entropy of the residual is significantly lower than that of the original image. Therefore, encoding of the residuals, instead of the original images, using entropy coding, such as Huffman codes and arithmetic codes, yields better compression in practice.Entropy coding of the residuals requires knowledge of their probability distribution. It can be observed that the distribution of residuals depends on the image's activity

Efficient Scalable 360-degree Video Compression Scheme using 3D Cuboid Partitioning

“…Thus, there is still open room for further improving SHVC compression efficiency by exploiting global redundancies while coding. With this aim, Murshed et al proposed cuboid (2D) based partitioning of the image where each image is segmented to arbitrary-shaped rectangular cuboids in such a way that obtained cuboids are maximum dissimilar in terms of pixel intensity distribution [18]- [21]. It was shown that 2D cuboid partitioning over video frames has excellent data compression capability due to exploiting global commonality features [22], [23].…”