Block-based intra-prediction is a key process of several state-of-the-art image and video codecs. Recently, supervised machine learning (ML) has improved this type of prediction. However, the performance of these ML-based methods depends on the amount, quality, and relevance of the training data. Furthermore, they require that the model’s parameters are stored so the imaging data can be reconstructed from the compressed bit-stream. Such a requirement, unfortunately, may hinder compression performance. This work departs from the current trend of training deep neural networks (NNs) and instead focuses on overfitted shallow NNs that can be optimized online. Specifically, it introduces the Reversible Regression Network (R2-Net) for block-based intra-prediction of imaging data. The R2-Net is an overfitted fully connected NN that can accurately predict imaging data while allowing for the recovery of such data by reversing the overfitting process. The R2-Net is optimized in an online manner based on parameters initialized to known values. Such an optimization strategy removes the need to store the model’s parameters. Performance evaluations on several benchmark video sequences show that the R2-Net outperforms the traditional block-based intra-prediction strategy with gains of up to 37.61 dB PSNR.

This paper presents a novel class of Graph-based Transform based on 3D convolutional neural networks (GBT-CNN) within the context of block-based predictive transform coding of imaging data. The proposed GBT-CNN uses a 3D convolutional neural network (3D-CNN) to predict the graph information needed to compute the transform and its inverse, thus reducing the signalling cost to reconstruct the data after transformation. The GBT-CNN outperforms the DCT and DCT /DST, which are commonly employed in current video codecs, in terms of the percentage of energy preserved by a subset of transform coefficients, the mean squared error of the reconstructed data, and the transform coding gain according to evaluations on several video frames and medical images.

This paper introduces the GBT-NN, a novel class of Graph-based Transform within the context of block-based predictive transform coding using intra-prediction. The GBT-NNis constructed by learning a mapping function to map a graph Laplacian representing the covariance matrix of the current block. Our objective of learning such a mapping functionis to design a GBT that performs as well as the KLT without requiring to explicitly com-pute the covariance matrix for each residual block to be transformed. To avoid signallingany additional information required to compute the inverse GBT-NN, we also introduce acoding framework that uses a template-based prediction to predict residuals at the decoder. Evaluation results on several video frames and medical images, in terms of the percentageof preserved energy and mean square error, show that the GBT-NN can outperform the DST and DCT.

Methods based on machine learning (ML) have been recently proposed to improve upon traditional block-based intra-prediction algorithms in modern video codecs [1,2]. Their performance, however, depends on the amount, quality and relevance of the training data. Furthermore, they require signaling the learned parameters to the decoder, thus increasing compressed data volumes. In this work, six new prediction modes based on fully-connected neural networks (FC-NNs) are proposed that avoid the two aforementioned shortcomings. To do so, 1-layer FC-NNs are used, whose parameters are rened by overfitting on the data samples being predicted. This allows to replicate the parameter optimization process at the decoder under a lossless compression regime without requiring any additional side information. Each proposed ML-based mode is based on a 1-layer FC-NN that predicts a block of size k k in a column-wise or row-wise manner using as input a subset of the reference samples used by traditional intra-prediction. Each subset, which varies for each column or row, is computed by averaging a number of reference samples to reduce noise [3]. Experimental results based on several video frames indicate that the proposed ML-based modes are selected as the best modes between 11% and 93% of the times. (see Table 1). When used in a hybrid intra-prediction framework that also includes HEVC's modes, the proposed ML-based modes increase prediction accuracy by between 0.56 dB and 7.01 dB PSNR, with respect to using only HEVC's modes.

This paper proposes a rate control (RC) algorithm for intra-coded sequences (I-frames) within the context of block-based predictive transform coding that departs from using trained models to approximate the rate-distortion (R-D) characteristics of the video sequence. Our algorithm employs piecewise linear approximations of the rate-distortion (R-D) curve of a frame at the block-level. Specifically, it employs information about the rate and distortion of already compressed blocks within the current frame to linearly approximate the slope of the R-D curve of each block. The proposed algorithm is implemented in the High-Efficiency Video Coding (H.265/HEVC) standard and compared with its current RC algorithm, which is based on a trained model. Evaluations on a variety of intra-coded sequences show that the proposed RC algorithm not only attains the overall target bit rate more accurately than the RC algorithm used by H.265/HEVC algorithm but is also capable of encoding each I-frame at a more constant bit rate according to the overall bit budget.

This paper introduces the GBT-L, a novel class of Graph-based Transform within the context of block-based predictive transform coding. The GBT-L is constructed using a 2D graph with unit edge weights and weighted self-loops in every vertex. The weighted selfloops are selected based on the residual values to be transformed. To avoid signalling any additional information required to compute the inverse GBT-L, we also introduce a coding framework that uses a template-based strategy to predict residual blocks in the pixel and residual domains. Evaluation results on several video frames and medical images, in terms of the percentage of preserved energy and mean square error, show that the GBT-L can outperform the DST, DCT and the Graph-based Separable Transform.

Digital pathology images are multi giga-pixel color images that usually require large amounts of bandwidth to be transmitted and stored. Lossy compression using intra-prediction offers an attractive solution to reduce the storage and transmission requirements of these images. In this paper, we evaluate the performance of the Graph-based Transform within the context of block-based predictive transform coding. To this end, we introduce a novel framework that eliminates the need to signal graph information to the decoder to recover the coefficients. This is accomplished by computing the GBT based on predicted residual blocks, which are predicted by a modeling approach that employs only the reference samples and information about the prediction mode. Evaluation results, in terms of the energy preserved when a small percentage of the largest coefficients are used for reconstruction of several pathology images, show that the GBT can outperform the DST and DCT.

We propose a novel Coding Unit (CU) level luma-chroma perceptually adaptive quantization technique for HEVC; we name this technique C-BAQ. C-BAQ is designed to improve upon the luma-only adaptive perceptual quantization method adopted by JCT-VC. This luma-only perceptual quantization technique, named AdaptiveQP, has demonstrated coding improvements compared with Uniform Reconstruction Quantization; it increases or decreases the Quantization Parameter (QP) of an entire 2N×2N CU based on the spatial activity of raw samples in the constituent luma Coding Block (CB). AdaptiveQP does not take into account the spatial activity of raw samples in the corresponding chroma Cb and chroma Cr CBs, however, which constitutes a significant shortcoming of the method. In contrast to AdaptiveQP, the proposed C-BAQ technique accounts for the spatial activity of the raw samples in both the luma CBs and also the chroma CBs when modifying the QP at the CU level. The primary objective with C-BAQ is as follows: to significantly decrease bitrates, in comparison with AdaptiveQP, without incurring a discernible loss of reconstruction quality.

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.

Whole-slide images (WSIs) are high-resolution, 2D, color digital images that are becoming valuable tools for pathologists in clinical, research and formative scenarios. However, their massive size is hindering their widespread adoption. Even though lossy compression can effectively reduce compressed file sizes without affecting subsequent diagnoses, no lossy coding scheme tailored for WSIs has been described in the literature. In this paper, a novel strategy called OptimizeMCT is proposed to increase the lossy coding performance for this type of images. In particular, an optimization method is designed to find image-specific multi-component transforms (MCTs) that exploit the high inter-component correlation present in WSIs. Experimental evidence indicates that the transforms yielded by OptimizeMCT consistently attain better coding performance than the Karhunen-Loève Transform (KLT) for all tested lymphatic, pancreatic and renal WSIs. More specifically, images reconstructed at the same bitrate exhibit average PSNR values 2.85~dB higher for OptimizeMCT than for the KLT, with differences of up to 5.17 dB.

High Efficiency Video Coding (HEVC), and its standardized extensions including Scalable HEVC (SHVC), contains a feature to enable default or custom Quantization Matrices (QMs). The default intra QM technique in HEVC, originally designed for the JPEG standard [1, 2], is based on a perceptual Human Visual System (HVS) Contrast Sensitivity Function (CSF) approach and its associated Modulation Transfer Function (MTF) [3]. Visual Display Units (VDUs), capable of displaying video data at High Definition (HD) and Ultra HD (UHD) display resolutions, are frequently utilized in a vast variety of technological domains. Compression artifacts caused by quantization, which are not noticeable in low display resolution environments, are clearly visible on HD and UHD video data and VDUs.

Predicting the future actions of individuals from geospatial data has the potential to provide a basis for tailored services. This work presents the Predictive Context Tree (PCT), a new hierarchical classifier based on the Context Tree summary model [8]. The PCT is capable of predicting the future contexts and locations of individuals to provide a basis for understanding not only where a user will be, but also what type of activity they will be performing. Through a comparison to established techniques, this paper demonstrates the applicability of the PCT by showing increased accuracies for location prediction, and increased utility through context prediction.

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree , a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees for use in applications where it is desirable to reduce the size of the tree while retaining useful information.

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.

This work defines an entropy-based measure aimed to establish the compression limits of wavelet-based image coding. This measure serves to appraise the efficiency of current codecs, determining whether there is margin for their improvement or not. Also, it may help to design new compression schemes that target a particular type of images and/or compression rates.

This works focuses on the High Efficiency Video Coding (HEVC) standard as a compression method to be potentially adopted by the Digital Imaging and Communications in Medicine (DICOM) standard. We are particularly interested in improving the lossless compression efficiency of the intra coding process for grayscale anatomical medical images. We focus on intra coding due to its low complexity and outstanding compression results, as well as the fact that it allows coding high-dimensional medical images on a slice-by-slice basis. This is especially advantageous for cases when only a small set of slices needs to be accessed without the need to decode the entire data set. Based on the characteristics of grayscale anatomical medical images, specifically their large amount of edge information and frequent number of patterns depicted on various directions, we propose improvements to HEVC intra coding based on sample-by-sample (SbS) differential pulse code modulation (DPCM) with equal displacements so the density of prediction modes is constant in all directions. Performance evaluations over MRI, CT and X-ray angiography sequences show that the proposed improvements outperform current HEVC lossless intra coding, achieving average coding gains of 6%.

DNA micro arrays are state-of-the-art tools in biological and medical research. In this work, we discuss the suitability of lossy compression for DNA micro array images and highlight the necessity for a distortion metric to assess the loss of relevant information. We also propose one possible metric that considers the basic image features employed by most DNA micro array analysis techniques. Experimental results indicate that the proposed metric can identify and differentiate important and unimportant changes in DNA micro array images.

Summary form only given. X-ray angiographic (angio) images are widely used for identifying irregularities in the vascular system. Because of their high spatial resolution and the increasingly amount of X-ray angio images generated, compression of these images is becoming increasingly appealing. In this paper, we introduce a diagnostically lossless compression scheme for X-ray angio images. The coding scheme relies on a novel method based on ray casting and a-shapes for distinguishing the clinically relevant Region of Interest from the background. The background is then suppressed to increase data redundancy, allowing to achieve a higher coding performance. Experimental results suggest that the proposed scheme correctly identifies the Region of Interest in X-ray angio images and achieves more than 2 bits per pixel reduction in average as compared to the case of compression with no background suppression. Results are reported here for 20 out of 25 images compressed using various lossless compression methods.