Entropy-based evaluation of context models for wavelet-transformed images

Entropy is a measure of a message uncertainty. Among others aspects, it serves to determine the minimum coding rate that practical systems may attain. This paper defines an entropy-based measure to evaluate context models employed in wavelet-based image coding. The proposed measure is defined considering the mechanisms utilized by modern coding systems. It establishes the maximum performance achievable with each context model. This helps to determine the adequateness of the model under different coding conditions and serves to predict with high precision the coding rate achieved by practical systems. Experimental results evaluate four well-known context models using different types of images, coding rates, and transform strategies. They reveal that, under specific coding conditions, some widely-spread context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based image codecs

Accelerating BPC-PaCo through visually lossless techniques

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo

Fast and Efficient Entropy Coding Architectures for Massive Data Compression

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented-more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems

Dual link image coding for earth observation satellites

The conventional strategy to download images captured by satellites is to compress the data on board and then transmit them via the downlink. It often happens that the capacity of the downlink is too small to accommodate all the acquired data, so the images are trimmed and/or transmitted through lossy regimes. This paper introduces a coding system that increases the amount and quality of the downloaded imaging data. The main insight of this paper is to use both the uplink and the downlink to code the images. The uplink is employed to send reference information to the satellite so that the onboard coding system can achieve higher efficiency. This reference information is computed on the ground, possibly employing extensive data and computational resources. The proposed system is called dual link image coding. As it is devised in this paper, it is suitable for Earth observation satellites with polar orbits. Experimental results obtained for data sets acquired by the Landsat 8 satellite indicate significant coding gains with respect to conventional methods