Soft context formation is a lossless image coding method for screen content. It encodes images pixel by pixel via arithmetic coding by collecting statistics for probability distribution estimation. Its main pipeline includes three stages, namely a context model based stage, a color palette stage and a residual coding stage. Each subsequent stage is only employed if the previous stage can not be applied since necessary statistics, e.g. colors or contexts, have not been learned yet. We propose the following enhancements: First, information from previous stages is used to remove redundant color palette entries and prediction errors in subsequent stages. Additionally, implicitly known stage decision signals are no longer explicitly transmitted. These enhancements lead to an average bit rate decrease of 1.07% on the evaluated data. Compared to VVC and HEVC, the proposed method needs roughly 0.44 and 0.17 bits per pixel less on average for 24-bit screen content images, respectively.

Screen content images typically contain a mix of natural and synthetic image parts. Synthetic sections usually are comprised of uniformly colored areas and repeating colors and patterns. In the VVC standard, these properties are exploited using Intra Block Copy and Palette Mode. In this paper, we show that pixel-wise lossless coding can outperform lossy VVC coding in such areas. We propose an enhanced VVC coding approach for screen content images using the principle of soft context formation. First, the image is separated into two layers in a block-wise manner using a learning-based method with four block features. Synthetic image parts are coded losslessly using soft context formation, the rest with VVC. We modify the available soft context formation coder to incorporate information gained by the decoded VVC layer for improved coding efficiency. Using this approach, we achieve Bjontegaard-Delta-rate gains of 4.98% on the evaluated data sets compared to VVC.

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed. One way to achieve this goal is to separate synthetic and natural regions. This paper proposes a segmentation method that identifies natural regions enabling better adaptive treatment. It supplements a compression method known as Soft Context Formation (SCF) and operates as a pre-processing step. If at least one natural segment is found within the SCI, it is split into two subimages (natural and synthetic parts) and the process of modelling and coding is performed separately for both. For SCIs with natural regions, the proposed method achieves a bit-rate reduction of up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF.

Probability distribution modeling is the basis for most competitive methods for lossless coding of screen content. One such state-of-the-art method is known as soft context formation (SCF). For each pixel to be encoded, a probability distribution is estimated based on the neighboring pattern and the occurrence of that pattern in the already encoded image. Using an arithmetic coder, the pixel color can thus be encoded very efficiently, provided that the current color has been observed before in association with a similar pattern. If this is not the case, the color is instead encoded using a color palette or, if it is still unknown, via residual coding. Both palette-based coding and residual coding have significantly worse compression efficiency than coding based on soft context formation. In this paper, the residual coding stage is improved by adaptively trimming the probability distributions for the residual error. Furthermore, an enhanced probability modeling for indicating a new color depending on the occurrence of new colors in the neighborhood is proposed. These modifications result in a bitrate reduction of up to 2.9 % on average. Compared to HEVC (HM-16.21 + SCM-8.8) and FLIF, the improved SCF method saves on average about 11 % and 18 % rate, respectively.

Competitive methods for lossless screen content coding are based on modelling of probability distributions. The most effective approach for losslessly compressing images with up to 90 000 colours is known as `soft context formation' (SCF). It scans the image to be compressed for repeating patterns and creates corresponding colour histograms. This yields excellent compression results as long as the actual pattern-based histogram contains the colour of the next pixel to be processed. If this colour is not contained, an exception handling is required by sending a special non-colour symbol (escape).This paper proposes an enhanced version of this exception handling coding. Instead of only using a pattern-based estimation, the probability of the escape symbol is now additionally modelled based on local dependencies of exception events. The combination of both estimates leads to 2.0% decrease of bitrate. In comparison to FLIF, FP8v3, and HEVC (HM-16.20+SCM-8.8), the entire SCF method achieves savings of about 26%, 8%, and 12% on average for images with less than 90 000 different colours (for 720p format).

The compression of screen content has attracted the interest of researchers in the last years as the market for transferring data from computer displays is growing. It has already been shown that especially those methods can effectively compress screen content which are able to predict the probability distribution of next pixel values. This prediction is typically based on a kind of learning process. The predictor learns the relationship between probable pixel colours and surrounding texture. Recently, an effective method called `soft context formation' (SCF) had been proposed which achieves much lower bitrates for images with less than 8 000 colours than other state-of-the-art compression schemes. This paper presents an enhanced version of SCF. The average lossless compression performance has increased by about 5% in application to images with less than 8 000 colours and about 10% for images with up to 90 000 colours. In comparison to FLIF, FP8v3, and HEVC (HM - 16.20 + SCM - 8.8), it achieves savings of about 33%, 4%, and 11% on average. The improvements compared to the original version result from various modifications. The largest contribution is achieved by the local estimation of the probability distribution for unpredictable colours in stage II of the compression scheme.

Images are typically nonstationary signals. If prediction is applied in a linear fashion, it must be combined with a technique that takes this characteristic into account. In general, images can either be regarded as piecewise 2-D autoregressive processes or they are handled in a blockwise manner. This paper presents a novel prediction technique, which treats the image data as an interleaved sequence generated by multiple sources. The challenge is to deinterleave the sequence and to compute prediction weights for each subsource separately. The proposed approach adaptively determines the subsources based on the textures surrounding the pixels. The new linear prediction technique is combined with template-matching prediction and a blending method that considers the correlation between the predictors' estimates is proposed. The prediction method is incorporated in a framework for lossless color image compression. In combination with an adaptive color transform and a dedicated coding algorithm, the proposed approach shows a competitive compression performance for a wide range of natural color images.

The efficient compression of color images requires a processing step exploiting the correlation between the color components. This is typically realized using a color transformation. In lossless compression systems, the reversible color transformation increases the bit-depth for chrominance components from eight to nine bits per pixels. This can be avoided by using modulo arithmetic, while keeping the property of reversibility. This letter investigates the impact of these modulo operations on the compression performance, compares different processing structures, and proposes a new adaptive selection of suitable color spaces. It is shown that (i) the limitation of the bit depth generally leads to lower compression performance, (ii) the drop in performance depends on the processing structure used, and (iii) the average performance can be improved by the proposed adaptive selection of the color space.

The contextual coding of data requires in general a step which reduces the vast variety of possible contexts down to a feasible number. This paper presents a new method for non-uniform quantisation of contexts, which adaptively merges adjacent intervals as long as the increase of the contextual entropy is negligible. This method is incorporated in a framework for lossless image compression. In combination with an automatic determination of model sizes for histogram-tail truncation, the proposed approach leads to a significant gain in compression performance for a wide range of different natural images.

Images are typically non-stationary signals. If prediction is applied in a linear fashion, it must be combined with a technique which takes this characteristic into account. In general, images can be either regarded as piecewise two-dimensional autoregressive processes or they are handled in a block-wise manner. This paper presents a novel prediction technique, which treats the image data as an interleaved sequence generated by multiple sources. The challenge is to de-interleave the sequence and to compute prediction weights for each sub-source separately. The proposed approach adaptively determines the sub-sources based on the textures within the images. The prediction method is incorporated in a framework for lossless image compression. It is based on least-mean-square filtering and achieves prediction-error entropies, which are comparable to those of least-squares approaches. In combination with a dedicated coding algorithm, the proposed approach shows a competitive compression performance for a wide range of different natural images.

The efficient compression of color images requires a step that takes the dependencies between the color components into account. This is mostly realized with a color transformation, mapping the red, green, and blue components into another representation. The transformation must be reversible if the compression system is to support the lossless reconstruction of the images. In this paper, we propose an entire family of multiplierless reversible color transforms and investigate their performance in lossless image compression. When using the LOCO-I algorithm or lossless JPEG2000 for compression, results show that the compression performance (in terms of bits per pixel) using the optimal color-space selection leads to a distinct reduction of the mean bitrate, compared to any fixed color space. Furthermore, it is shown that it is possible to automatically select a suitable color space without significant loss in compression efficiency, compared to the optimal selection. This automatic selection improves the compression performance independently of whether LOCO-I or JPEG2000 is applied.

Knowledge of the shape of crystals can benefit data collection in X-ray crystallography. A preliminary step is the determination of the loop object, i.e., the shape of the loop holding the crystal. Based on the standard set-up of experimental X-ray stations for protein crystallography, the paper reviews a reconstruction method merely requiring 2D object contours and presents a dedicated novel algorithm. Properties of the object surface (e.g., texture) and depth information do not have to be considered. The complexity of the reconstruction task is significantly reduced by slicing the 3D object into parallel 2D cross-sections. The shape of each cross-section is determined using support lines forming polygons. The slicing technique allows the reconstruction of concave surfaces perpendicular to the direction of projection. In spite of the low computational complexity, the reconstruction method is resilient to noisy object projections caused by imperfections in the image-processing system extracting the contours. The algorithm developed here has been successfully applied to the reconstruction of shapes of loop objects in X-ray crystallography.

This paper proposes a novel design method for 3-channel filter banks based on the lifting scheme. The resulting filter bank is able to map integer signal values to integer sub-band values. The design also offers low-complexity handling of the signal boundaries. In application to image data compression, the new integer wavelet decomposition shows competitive performance compared to the reversible 5/3 transform used in JPEG2000.