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DCC 2021Virtual Conference - The Data Compression Conference (DCC) is an international forum for current work on data compression and related applications. Both theoretical and experimental work are of interest. Visit DCC 2021 website

This paper analyzes the average behavior of video streaming systems with adaptation to network bandwidth and player sizes. The main results are formulae for average system performance parameters for given models of codecs, content, players, and networks. Derived expressions are used to study performance limits achievable by adaptive streaming systems, and pose several related optimization problems. Numerical simulations, illustrating the usefulness of the proposed formulae and techniques are also provided

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This paper presents a software-based method for estimating the power consumption of video decoders on various Android devices. Using this method, we developed an automatic system that consists of the VEQE Android application to measure the power consumption of video decoders and a server to collect the metrics. The system allowed us to create power-consumption and decoding-speed dataset for video decoders operating on 236 devices, representing 147 models.

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Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. One approach to compress point clouds is the video based point cloud compression (V-PCC) technique which projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using the coding tools offered by modern video coding standard like HEVC.

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Recently it was shown (Puglisi and Zhukova, Proc. SPIRE, 2020) that the suffix array (SA) data structure can be effectively compressed with relative Lempel-Ziv (RLZ) dictionary compression in such a way that arbitrary subar- rays can be rapidly decompressed, thus facilitating compressed indexing. In this paper we describe optimizations to RLZ-compressed SAs, including generation of more effective dictionaries and compact encodings of index components, both of which reduce index size without adversely affecting subarray access speeds relative to other compressed indexes.

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We describe methods to support fast rank queries on the Burrows-Wheeler transform (BWT) string S of an input string T on alphabet Σ, in order to support pattern counting queries. Our starting point is an approach previously adopted by several authors, which is to represent S as |Σ| bitvectors, where the bitvector for symbol c has a 1 at position i if and only if S[i] = c, with the bitvec- tors stored in Elias-Fano (EF) encodings, to enable binary rank queries. We first show that the clustering of symbols induced by the BWT makes standard implementations of EF unattractive.

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We focus on the Multi-Rate Sampling (MRS) Compressed Sensing (CS) scheme, in which several Analog-to-Digital Converters (ADC) sample in parallel at different sub-Nyquist rates. In this paper, we postulate that a good signal recovery requires the measurement matrix rank (MMR) to be as high as possible. We also present an upper-bound for the MMR. Choosing pairwise coprime sampling rates allows to reach this upper-bound.

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