There is a class of entropy-coding methods which do not substitute symbols by code words (such as Huffman coding), but operate on intervals or ranges and thus allow a better approximation of the data entropy. This class includes three prominent members: conventional arithmetic coding, range coding, and coding based on asymmetric numeral systems. To determine the correct symbol in the decoder, each of these methods requires the comparison of a state variable with subinterval boundaries.

In adaptive operation, considering varying symbol statistics, an array of interval boundaries must additionally be kept up to date. The larger the symbol alphabet, the more time-consuming both the search for the correct subinterval and the updating of interval borders become. These entropy coding methods play an important role in all data transmission and storage applications, and optimising speed can be crucial.

Based on detailed pseudo-code, different known and proposed approaches are discussed to speed up the symbol search in the decoder and the adaptation of the array of interval borders, both depending on the chosen alphabet size. It is shown that reducing the big O complexity in practical implementations does not necessarily lead to an acceleration, especially if the alphabet size is too small. For example, the symbol determination at the decoder shows an expected low cpu-clock ratio (O(logn) algorithm versus O(n) algorithm) of about 0.62 for an alphabet with 256 symbols. However, for an alphabet with only 4 symbols, this ratio is 1.05, that means the algorithm with lower theoretical complexity executes slightly faster here. In adaptive compression mode, the binary indexing (BI) method proves to be superior when considering the overall processing time. Although the symbol search (in the decoder) takes longer than using other algorithms (e.g. cpu-clock ratio BI/O(logn) is 1.57), the faster updating of the array of interval borders more than compensates for this disadvantage (total ratio BI/O(logn) is 0.72). A variant of the binary indexing method is proposed, which is more flexible and has a partially lower complexity than the original approach. Specifically, the rescaling of cumulative counts can be reduced in its complexity from O(4n+[log2(n)−2]·n/2) to O(3n).

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.

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 stage is only employed if the previous stage is impossible 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 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 1.16% on the evaluated data. Compared to FLIF and HEVC, the proposed method needs roughly 0.28 and 0.17 bits per pixel less on average for 24-bit screen content images, respectively.

The abbreviation rANS stands for a relatively new method of arithmetic coding based on asymmetric numeral systems (ANS) which combines the advantages of arithmetic coding in terms of performance and the advantages of Huffman coding in terms of speed.
Compared to conventional arithmetic coding methods, the mathematical apparatus is slightly different which has the consequence that the decoding order is reversed to the encoding order, i.e. the processing follows the last-in-first-out principle.
This makes it somewhat difficult to design the coding process to adapt to changing symbol statistics, and therefore rANS coding has so far only been applied in settings with fixed statistics.
In particular, the frequent rescaling of statistics required to reduce the influence of old symbols becomes a problem when the order of processing is different on the encoder and decoder sides.

This paper proposes a new method that allows adaptive coding within the framework of rANS coding and additionally offers the possibility of rescaling the symbols frequencies. Investigations show that this method enables the same compression performance for rANS as for conventional arithmetic coding.

Distance transformation is an image processing technique used for many different
applications. Related to a binary image, the general idea is to determine the distance of
all background points to the nearest object point (or vice versa). In this tutorial, different
approaches are explained in detail and compared using examples. Corresponding source
code is provided to facilitate own investigations. A particular objective of this tutorial
is to clarify the difference between arbitrary distance transforms and exact Euclidean
distance transformations.

Finding the shortest tour visiting all given points at least ones belongs to the most famous optimization problems until today (TSP . . . travelling salesman problem). Optimal solutions exist for many problems up to several ten thousand points. The major difficulty in solving larger problems is the required computational complexity. This shifts the research from finding the optimum with no time limitation to approaches that find good but sub-optimal solutions in pre-defined limited time. This paper proposes a new approach for two-dimensional symmetric problems with more than a million coordinates that is able to create good initial tours within few minutes. It is based on a hierarchical clustering strategy and supports parallel processing. In addition, a method is proposed that can correct unfavourable paths with moderate computational complexity. The new approach is superior to state-of-the-art methods when applied to TSP instances with non-uniformly distributed coordinates.

Advanced object detectors based on Convolutional Neural Networks (CNNs) offer high detection rates for many application scenarios but only within their respective training, validation and test data. Recent studies show that such methods provide a limited generalization ability for unknown data, even for small image modifications including a limited scale invariance. Reliable person detection with aerial robots (Unmanned Aerial Vehicles, UAVs) is an essential task to fulfill high security requirements or to support robot control, communication, and human-robot interaction. Particularly in an agricultural context persons need to be detected from a long distance and a high altitude to allow the UAV an adequate and timely response. While UAVs are able to produce high resolution images that enable the detection of persons from a longer distance, typical CNN input layer sizes are comparably low. The inevitable scaling of images to match the input-layer size can lead to a further reduction in person sizes. We investigate the reliability of different YOLOv3 architectures for person detection in regard to those input-scaling effects. The popular VisDrone data set with its varying image resolutions and relatively small depiction of humans is used as well as high resolution UAV images from an agricultural data set. To overcome the scaling problem, an algorithm is presented for segmenting high resolution images in overlapping tiles that match the input-layer size. The number and overlap of the tiles are dynamically determined based on the image resolution. It is shown that the detection rate of very small persons in high resolution images can be improved using this tiling approach.