This letter proposes methods to improve data size and access time for$k^{2}$-raster, a losslessly compressed data structure that provides efficient storage and real-time processing. Hyperspectral scenes from real missions are used as our testing data. In previous studies, with$k^{2}$-raster, the size of the hyperspectral data was reduced by up to 52% compared with the uncompressed data. In this letter, we continue to explore novel ways of further reducing the data size and access time. First, we examine the possibility of using the raster matrix of hyperspectral data without any padding (unpadded matrix) while still being able to compress the structure and access the data. Second, we examine some integer encoders, more specifically the Simple family. We discuss their ability to provide random element access and compare them with directly addressable codes (DACs), the integer encoder used in the original description for$k^{2}$-raster. Experiments show that the use of unpadded matrices has improved the storage size up to 6% while the use of a different integer encoder reduces the storage size up to 6% and element access time up to 20%.

The increase in popularity of point-cloud-oriented applications has triggered the development of specialized compression algorithms. In this paper, a novel algorithm is developed for the lossless geometry compression of voxelized point clouds following an intra-frame design. The encoded voxels are arranged into runs and are encoded through a single-pass application directly on the voxel domain. This is done without representing the point cloud via an octree nor rendering the voxel space through an occupancy matrix, therefore decreasing the memory requirements of the method. Each run is compressed using a context-adaptive arithmetic encoder yielding state-of-the-art compression results, with gains of up to 15% over TMC13, MPEG's standard for point cloud geometry compression. Several proposed contributions accelerate the calculations of each run's probability limits prior to arithmetic encoding. As a result, the encoder attains a low computational complexity described by a linear relation to the number of occupied voxels leading to an average speedup of 1.8 over TMC13 in encoding speeds. Various experiments are conducted assessing the proposed algorithm's state-of-the-art performance in terms of compression ratio and encoding speeds.

Point cloud data have been put under the spotlight by many applications that play an increasingly important role in our every day lives. Their large size and ever-growing prevalent use cases have raised the interest in specialized compression algorithms for point cloud data. In this paper we propose a lossless intra-frame encoder for point cloud geometry. It relies on a single projection of the entire point cloud on a predetermined plane, combined with a context-adaptive binary arithmetic encoder. Our approach simplifies the current best performing approach for intra-frame compression. The experimental results indicate that our proposal not only improves the performance of all other intra-frame approaches, but it even surpasses the performance of state-of-the-art inter-frame approaches. Furthermore, we suggest to replace the adaptive encoder with a semi-adaptive approach for further performance gains.

Biorthogonal graph wavelet filterbanks, also known as GraphBior, are one of the most popular graph transforms used in image compression, but up to now, they could be designed based on two known admissible fundamental matrices: i) the random walk Laplacian, which heavily penalizes low degree pixels, and ii) the normalized Laplacian, which lacks a zero-DC response. By exploiting a new extension of the admissibility condition in GraphBior we propose a new fundamental matrix with the goal of distributing the errors of GraphBior more uniformly across pixels with different node degrees. Furthermore the proposed matrix preserves high energy compaction linked to the zero-DC GraphBior variation.