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.

The coding and transmission of the massive datasets captured by Earth Observation (EO) satellites is a critical issue in current missions. The conventional approach is to use compression on board the satellite to reduce the size of the captured images. This strategy exploits spatial and/or spectral redundancy to achieve compression. Another type of redundancy found in such data is the temporal redundancy between images of the same area that are captured at different instants of time. This type of redundancy is commonly not exploited because the required data and computing power are not available on board the satellite. This paper introduces a coding scheme for EO satellites able to exploit this redundancy. Contrary to traditional approaches, the proposed scheme employs both the downlink and the uplink of the satellite. Its main insight is to compute and code the temporal redundancy on the ground and transmit it to the satellite via the uplink. The satellite then uses this information to compress more efficiently the captured image. Experimental results for Landsat 8 images indicate that the proposed dual link image coding scheme can achieve higher coding performance than traditional systems for both lossless and lossy regimes.

Recent years have seen the upraising of a new type of processors strongly relying on the Single Instruction, Multiple Data (SIMD) architectural principle. The main idea behind SIMD computing is to apply a flow of instructions to multiple pieces of data in parallel and synchronously. This permits the execution of thousands of operations in parallel, achieving higher computational performance than with traditional Multiple Instruction, Multiple Data (MIMD) architectures. The level of parallelism required in SIMD computing can only be achieved in image coding systems via microscopic parallel strategies that code multiple coefficients in parallel. Until now, the only way to achieve microscopic parallelism in bit plane coding engines was by executing multiple coding passes in parallel. Such a strategy does not suit well SIMD computing because each thread executes different instructions. This paper introduces the first bit plane coding engine devised for the fine grain of parallelism required in SIMD computing. Its main insight is to allow parallel coefficient processing in a coding pass. Experimental tests show coding performance results similar to those of JPEG2000.

Wavelet-based coding systems commonly employ uniform scalar deadzone quantization (USDQ) together with a bitplane coding strategy to progressively refine image quality. Our previous work presents a quantization scheme that employs 2 step sizes depending on the magnitude of the coefficients. This 2-step scalar deadzone quantization (2SDQ) scheme is introduced in the framework of JPEG2000 by modifying all coefficients within a codeblock to enhance the quality of the image while transmitting fewer bitplanes than those needed with a conventional USDQ scheme. This paper extends our prior work by applying the 2SDQ in a subblock level, i.e., in small sets of coefficients, called cells, selected within a codeblock. Combined with rate-distortion optimization techniques, the proposed cell-based 2SDQ can help to code high quality images employing even fewer bitplanes than those needed with our previous strategy. This may be especially useful for high-dynamic range images or for devices with constrained resources.

DNA micro arrays are useful to identify the function and regulation of a large number of genes in a single experiment, even whole genomes. In this work, we analyze the relationship between DNA micro array image histograms and the compression performance of loss less JPEG2000. Also, a reversible transform based on histogram swapping is proposed. Intensive experimental results using different coding parameters are discussed. Results suggest that this transform improves previous loss less JPEG2000 results on all DNA micro array image sets.

Embedded quantization is a mechanism employed by lossy image coding systems to successively refine the distortion of an image. Commonly, it is conducted through a uniform scalar dead zone quantizer (USDQ) together with a bitplane coding strategy (BPC). Although this scheme is convenient for current hardware architectures and achieves competitive coding performance, it establishes the embedded quantizer without allowing major variations. This paper studies the design of non-restricted embedded quantizers with the aim to determine a quantization scheme that provides (near-)optimal performance for the lossy compression of images at low rates. Results suggest that optimally designed quantization schemes can achieve slightly better performance than that of USDQ+BPC by employing a non-uniform quantizer that requires fewer quantization stages.

The RKLT is a lossless approximation to the KLT, and has been recently employed for progressive lossy-to-lossless coding of hyperspectral images. Both yield very good coding performance results, but at a high computational price. In this paper we investigate two RKLT clustering approaches to lessen the computational complexity problem: a normal clustering approach, which still yields good performance; and a multi-level clustering approach, which has almost no quality penalty as compared to the original RKLT. Analysis of rate-distortion evolution and of lossless compression ratio is provided. The proposed approaches supply additional benefits, such as spectral scalability, and a decrease of the side information needed to invert the transform. Furthermore,since with a clustering approach, SERM factorization coefficients are bounded to a finite range, the proposed methods allow coding of large three dimensional images within JPEG2000.