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).

Die zunehmende Bedeutung der Photovoltaik für die deutsche Energieversorgung erfordert eine zuverlässige und effiziente Leistungsverifizierung, um mögliche Ertragseinbußen zu minimieren. Smart-Wechselrichter mit integrierten Ferndiagnosefunktionen bieten eine vielversprechende Lösung für die Überwachung und Optimierung des Anlagenbetriebs.

 Dieser Beitrag stellt einen speziell entwickelten Messaufbau vor, der die Überprüfung der Messfähigkeit von Smart-Wechselrichtern ermöglicht, insbesondere in Hinblick auf die Messung von Strom-Spannungs-Kennlinien (IU-Kennlinien). Der Fokus der Untersuchung liegt auf der Analyse der Messunsicherheit gemessener Leistungsmaxima durch Referenzieren auf kalibrierte IU-Kennlinienschreiber.

 Die durchgeführten Vergleichsmessungen liefern zufriedenstellende Ergebnisse und zeigen einen Trend zu geringen Messunsicherheiten für höhere Bestrahlungsstärken. Dies bestätigt das Potenzial von Smart-Wechselrichtern für die Leistungsüberwachung von Photovoltaikanlagen und unterstreicht den Nutzen für die Optimierung des Anlagenbetriebs.

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.

Die präzise Erkennung von Clear-Sky-Momenten ist für die Überwachung und Effizienzana-lyse von Photovoltaikanlagen von zentraler Bedeutung, da zu diesen Zeitpunkten definierte und model-lierbare Einstrahlungsverhältnisse herrschen. Es wird ein hybrides Modell zur verbesserten Erkennung von Clear-Sky-Momenten auf Basis von Einstrahlungsdaten vorgestellt. Hierfür wurden zunächst ma-nuell, dann mithilfe eines CNNs Merkmale aus den Einstrahlungsdaten gebildet. Eine Falls tudie mit Referenzdaten belegt, dass durch die Kombination dieser wissens-und datengetriebenen Methoden Clear-Sky-Momente zuverlässiger identifiziert werden können. Dadurch können Analysemethoden schneller und zuverlässiger Aussagen über die untersuchten PV-Anlagen treffen.