1. State-of-the-art trends in data compression : COMPROMISE case studyDavid Podgorelec, Damjan Strnad, Ivana Kolingerová, Borut Žalik, 2024, izvirni znanstveni članek Opis: After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight. In this article, we try to critically evaluate the most prominent of these trends and to explore their parallels, complementarities, and differences. Digital data restoration mimics the human ability to omit memorising information that is satisfactorily retrievable from the context. Feature-based data compression introduces a two-level data representation with higher-level semantic features and with residuals that correct the feature-restored (predicted) data. The integration of the advantages of individual domain-specific data compression methods into a general approach is also challenging. To the best of our knowledge, a method that addresses all these trends does not exist yet. Our methodology, COMPROMISE, has been developed exactly to make as many solutions to these challenges as possible inter-operable. It incorporates features and digital restoration. Furthermore, it is largely domain-independent (general), asymmetric, and universal. The latter refers to the ability to compress data in a common framework in a lossy, lossless, and near-lossless mode. COMPROMISE may also be considered an umbrella that links many existing domain-dependent and independent methods, supports hybrid lossless–lossy techniques, and encourages the development of new data compression algorithms
Ključne besede: data compression, data resoration, universal algorithm, feature, residual
Objavljeno v DKUM: 04.02.2025; Ogledov: 0; Prenosov: 7
 Celotno besedilo (1,13 MB) |
2. Efficient compressed storage and fast reconstruction of large binary images using chain codesDamjan Strnad, Danijel Žlaus, Andrej Nerat, Borut Žalik, 2024, izvirni znanstveni članek Opis: Large binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between and faster reconstruction on test data
Ključne besede: binary mask, machine learning, chain code, binary encoding, bitmap reconstruction
Objavljeno v DKUM: 29.01.2025; Ogledov: 0; Prenosov: 7
Celotno besedilo (1,45 MB) |
3. Efficient encoding and decoding of voxelized models for machine learning-based applicationsDamjan Strnad, Štefan Kohek, Borut Žalik, Libor Váša, Andrej Nerat, 2025, izvirni znanstveni članek Opis: Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. In order to reduce high space requirements and the effect of noise in the data, point clouds are often transformed to a structured representation such as a voxel grid. Storing, transmitting and consuming voxelized geometry, however, remains a challenging problem for machine learning pipelines running on devices with limited amount of on-chip memory with low access latency. A viable solution is to store the data in a compact encoded format, and perform on-the-fly decoding when it is needed for processing. Such on-demand expansion must be fast in order to avoid introducing substantial additional delay to the pipeline. This can be achieved by parallel decoding, which is particularly suitable for massively parallel architecture of GPUs on which the majority of machine learning is currently executed. In this paper, we present such method for efficient and parallelizable encoding/decoding of voxelized geometry. The method employs multi-level context-aware prediction of voxel occupancy based on the extracted binary feature prediction table, and encodes the residual grid with a pointerless sparse voxel octree (PSVO). We particularly focused on encoding the datasets of voxelized trees, obtained from both synthetic tree models and LiDAR point clouds of real trees. The method achieved 15.6% and 12.8% reduction of storage size with respect to plain PSVO on synthetic and real dataset, respectively. We also tested the method on a general set of diverse voxelized objects, where an average 11% improvement of storage space was achieved.
Ključne besede: voxel grid, feature prediction, tree models, prediction-based encoding, key voxels, residuals, sparse voxel octree
Objavljeno v DKUM: 09.01.2025; Ogledov: 0; Prenosov: 5
Celotno besedilo (20,93 MB) |
4. A case study on entropy-aware block-based linear transforms for lossless image compressionBorut Žalik, David Podgorelec, Ivana Kolingerová, Damjan Strnad, Štefan Kohek, 2024, izvirni znanstveni članek Opis: Data compression algorithms tend to reduce information entropy, which is crucial, especially in the case of images, as they are data intensive. In this regard, lossless image data compression is especially challenging. Many popular lossless compression methods incorporate predictions and various types of pixel transformations, in order to reduce the information entropy of an image. In this paper, a block optimisation programming framework is introduced to support various experiments on raster images, divided into blocks of pixels. Eleven methods were implemented within , including prediction methods, string transformation methods, and inverse distance weighting, as a representative of interpolation methods. Thirty-two different greyscale raster images with varying resolutions and contents were used in the experiments. It was shown that reduces information entropy better than the popular JPEG LS and CALIC predictors. The additional information associated with each block in is then evaluated. It was confirmed that, despite this additional cost, the estimated size in bytes is smaller in comparison to the sizes achieved by the JPEG LS and CALIC predictors.
Ključne besede: computer science, information entropy, prediction, inverse distance transform, string transformations
Objavljeno v DKUM: 07.01.2025; Ogledov: 0; Prenosov: 8
Celotno besedilo (5,13 MB) |
5. 3D verižne kode F26, F6 in 5OT za opis skeletov vokseliziranih objektovGoran Pjević, 2024, magistrsko delo Opis: V magistrskem delu opišemo tehnike za tvorbo 3D verižnih kod, primernih za opis skeletov vokseliziranih geometrijskih objektov. Osredotočimo se na Freemanovi verižni kodi v 26 in 6 smeri (F26 in F6) ter 5-ortogonalno verižno kodo (5OT). Te verižne kode nato uporabimo za stiskanje 3D skeletov. Verižne kode stisnemo z algoritmi stiskanja RLE, LZW, bzip2 in bzip3 ter primerjamo njihovo uspešnost.
Ključne besede: 3D geometrijski objekti, brezizgubno stiskanje podatkov, primerjava metod RLE, LZW, bzip2 in bzip3
Objavljeno v DKUM: 23.12.2024; Ogledov: 0; Prenosov: 17
Celotno besedilo (1,80 MB) |
6. Novel GPU-accelerated high-resolution solar potential estimation in urban areas by using a modified diffuse irradiance modelNiko Lukač, Domen Mongus, Borut Žalik, Gorazd Štumberger, Marko Bizjak, 2024, izvirni znanstveni članek Opis: In the past years various methods have been developed to estimate high-resolution solar potential in urban areas, by simulating solar irradiance over surface models that originate from remote sensing data. In general, this requires discretisation of solar irradiance models that estimate direct, reflective, and diffuse irradiances. The latter is most accurately estimated by an anisotropic model, where the hemispherical sky dome from arbitrary surface’s viewpoint consists of the horizon, the circumsolar and sky regions. Such model can be modified to incorporate the effects of shadowing from obstruction with a view factor for each sky region. However, state-of-the-art using such models for estimating solar potential in urban areas, only considers the sky view factor, and not circumsolar view factor, due to high computational load. In this paper, a novel parallelisation of solar potential estimation is proposed by using General Purpose computing on Graphics Processing Units (GPGPU). Modified anisotropic Perez model is used by considering diffuse shadowing with all three sky view factors. Moreover, we provide validation based on sensitivity analysis of the method’s accuracy with independent meteorological measurements, by changing circumsolar sky region’s half-angle and resolution of the hemispherical sky dome. Finally, the presented method using GPPGU was compared to multithreaded Central Processing Unit (CPU) approach, where on average a 70x computational speedup was achieved. Finally, the proposed method was applied over a urban area, obtained from Light Detection And Ranging (LiDAR) data, where the computation of solar potential was performed in a reasonable time.
Ključne besede: solar energy, solar potential, anisotropic diffuse irradiance, LiDAR, GPGPU
Objavljeno v DKUM: 17.12.2024; Ogledov: 0; Prenosov: 4
Celotno besedilo (8,06 MB) |
7. Region segmentation of images based on a raster-scan paradigmLuka Lukač, Andrej Nerat, Damjan Strnad, Štefan Horvat, Borut Žalik, 2024, izvirni znanstveni članek Opis: This paper introduces a new method for the region segmentation of images. The approach is based on the raster-scan paradigm and builds the segments incrementally. The pixels are processed in the raster-scan order, while the construction of the segments is based on a distance metric in regard to the already segmented pixels in the neighbourhood. The segmentation procedure operates in linear time according to the total number of pixels. The proposed method, named the RSM (raster-scan segmentation method), was tested on selected images from the popular benchmark datasets MS COCO and DIV2K. The experimental results indicate that our method successfully extracts regions with similar pixel values. Furthermore, a comparison with two of the well-known segmentation methods—Watershed and DBSCAN—demonstrates that the proposed approach is superior in regard to efficiency while yielding visually similar results.
Ključne besede: segment, image analysis, distance metric, Watershed, DBSCAN
Objavljeno v DKUM: 05.12.2024; Ogledov: 0; Prenosov: 3
 Povezava na datoteko |
8. Jedrnat zapis redkih matrikKlemen Golob, 2024, diplomsko delo Opis: V diplomskem delu opisujemo postopke in implementacijo metod stiskanja redkih matrik. Implementirali smo metode CSR (angl. Compressed Sparse Row), CSF (angl. Coordinate Storage Format), CSV (angl. Compressed Sparse Vector), MSF (angl. Modified Storage Format) in CC (angl. Coordinate Compression). Kot primere redkih matrik smo uporabili decimirane sivinske rastrske slike. Po predstavitvi elementov redke matrike z omenjenimi metodami smo dobljeno zaporedje stisnili z aritmetičnim kodiranjem in z algoritmoma Gzip ter bzip2. Eksperimenti so pokazali, da je metoda CSV najučinkovitejša izmed opisanih metod.
Ključne besede: stiskanje podatkov, stiskanje koordinat, metode stiskanja matrik CSR, CSF, CSV, MSF, CC.
Objavljeno v DKUM: 19.09.2024; Ogledov: 0; Prenosov: 25
Celotno besedilo (2,05 MB) |
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