This paper presents hardware-oriented lossless color filter array (CFA) image compression algorithm. Presented algorithm and hardware implementation in Verilog Hardware Description Language are based on existing solutions with additional elements increasing the efficiency of the algorithms. In particular, the new method of parameter values calculation for the Golomb-Rice encoding and the zero run-length encoding has been added. HDL code was written without any primitives, so it can be synthesized independently for each platform. In exchange for a slight increase in computational complexity, it was possible to reduce required bit rate by an average of 7.2% when testing on a set containing endoscopy images. This paper presents elements of other work on which the algorithm is based and characterizes specific optimizations and their impact on the final algorithm result. Finally, these results will be compared with other available solutions.
PL
W artykule przedstawiono sprzętowo zorientowany algorytm bezstratnej kompresji obrazów w formacie color filter array (CFA). Przedstawiony algorytm oraz implementacja sprzętowa w języku opisu sprzętu Verilog bazują na istniejących rozwiązaniach dodając do nich dodatkowe elementy zwiększające efektywność algorytmów. W szczególności zastosowano nową metodę obliczania wartości parametrów dla kodowania Golomba-Rice’a oraz dodano kodowania długości ciągu zer. Kod HDL został napisany bez żadnych predefiniowanych bloków, dzięki temu może być syntezowany niezależnie dla każdej platformy. W zamian za niewielki wzrost złożoności obliczeniowej udało się zwiększyć zredukować wymaganą przepływność łącza średnio o 7.2% na zbiorze testowym zawierającym obrazy z endoskopii. W artykule przedstawiono elementy innych prac, na których opiera się algorytm, oraz scharakteryzowano poszczególne optymalizacje i ich wpływ na ostateczny wynik algorytmu. Na koniec wyniki te zostaną porównane z innymi dostępnymi rozwiązaniami.
Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved image quality assessment techniques for wireless capsule endoscopy applications. The proposed objective IQA methods are obtained by modifying the existing full-reference image quality assessment techniques. The modification is done by excluding the noninformative regions, in endoscopic images, in the computation of IQA metrics. The experimental results demonstrate that the proposed IQA method gives an improved peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM). The proposed image quality assessment methods are more reliable for compressed endoscopic capsule images.
Interactive data visualisations are popular and come in different forms, from static raster files to dynamic and complex proposals. The usability of the applications can be increased through the improvement of their technical attributes such as a reduced number of external components or image file compression. The objective of the paper is to analyse the correlation between raster compression and the performance of a map application. The research involved an application created specifically for this purpose. Raster compression and performance were measured using selected online tools. Raster compression has been demonstrated to enhance map application performance, although not all performance indicators improved.
By combining a wavelet transform with chaos scrambling, an image compression and encryption algorithm based on 2D compressive sensing is designed. The wavelet transform is employed to obtain the sparse representation of a plaintext image. The sparse image is measured in two orthogonal directions by compressive sensing. Then, the result of 2D compressive sensing is confused by the Arnold transform and the random pixel scrambling. The combination of four-dimensional chaos and logistic map is exploited to generate the first row of the key-controlled circulant matrix. The proposed algorithm not only carries out image compression and encryption simultaneously, but also reduces the consumption of the key by controlling the generation of measurement matrix. Experimental results reveal that the proposed image compression and encryption algorithm is resistant to noise attacks with good compression performance and high key sensitivity.
An image compression and encryption algorithm by combining the advanced encryption standard (AES) with the hyper-chaotic system is designed, in which Arnold map is employed to eliminate part of the block effect in the image compression process. The original image is compressed with the assistance of a discrete cosine transform and then its transform coefficients are encrypted with the AES algorithm. Besides, the hyper-chaotic system is adopted to introduce the nonlinear processfor image encryption. Numerical simulations and theoretical analyses demonstrate that the proposed image compression and encryption algorithm is of high security and good compression performance.
Based on compressive sensing and log operation, a new image compression-encryption algorithm is proposed, which accomplishes encryption and compression simultaneously. The proposed image compression-encryption algorithm takes advantage of not only the physical realizability of partial Hadamard matrix, but also the resistance of the chosen-plaintext attack since all the elements in the partial Hadamard matrix are 1, –1 or log 1 = 0. The proposed algorithm is sensitive to the key and it can resist various common attacks. The simulation results verify the validity and reliability of the proposed image compression-encryption algorithm.
Interactive medical teleconsultations are an important tool in modern medical practice. Their applications include remote diagnostics, conferences, work- shops, and classes for students. In many cases, standard medium or low-end machines are employed, and the teleconsultation systems must be able to provide a high quality of user experience with very limited resources. Particularly problematic are large datasets consisting of image sequences that need to be accessed fluently. The main issue is insufficient internal memory; therefore, proper compression methods are crucial. However, a scenario where image sequences are kept in a compressed format in the internal memory and decompressed on- the-fly when displayed is difficult to implement due to performance issues. In this paper, we present methods for both lossy and lossless compression of medical image sequences that only require compatibility with the Pixel Shader 2.0 standard, which is present even on relatively old, low-end devices. Based on the evaluation of quality, size reduction, and performance, these methods have been proven to be suitable and beneficial for medical teleconsultation applications
Pod pojęciem kompresji obrazów kryją się najróżniejsze algorytmy kompresji danych. Najczęściej są to algorytmy kompresji stratnej, które charakteryzują się znacznym stopniem kompresji, lecz ich wadą jest utrata informacji podczas procesu kompresji. W niniejszym artykule zostanie opisany algorytm kompresji fraktalnej, który zalicza się do algorytmów kompresji stratnej i najczęściej wykorzystywany jest przy kompresji obrazów. Zostanie również przedstawiony opisu działania algorytmu z wykorzystaniem systemu funkcji iterowanych oraz jego wady i zalety. Celem artykułu jest porównanie możliwości kompresji metodą fraktalną z najpopularniejszą obecnie metodą kompresji stratnej obrazów JPEG. Uzyskane dane posłużą do dalszej analizy możliwości algorytmu kompresji fraktalnej oraz metod jego optymalizacji.
EN
The term image compression hides various data compression algorithms. Usually these are lossy compression algorithms, which characterize high degree of compression, but their disadvantage is the loss of information during compression process. The article describes fractal compression algorithm, which is one of the lossy compression algorithms and is used mostly in image compression. It will be also described algorithm details using iterated function system and its advantages and disadvantages. The aim of the article is to compare the capabilities of fractal compression method with JPEG which is currently the most popular method of lossy image compression. The obtained data will be used to further analyze possibilities of fractal compression and methods of its optimalization.
Actuality of problem in the improvement of transport safety at level crossings (LC) is caused by increasing the number of vehicles and reducing discipline of vehicle drivers. One of ways for solution of this problem is associated with using the video surveillance systems for monitoring danger area of level crossing. In such systems due to the limited bandwidth of data channel usually the image compression techniques are used. In this paper the pre-processing method for compression of images is presented. Proposed method accounts unequal subjective informational content of different LC image regions (using fuzzy logic and wavelet transform). Comparison of this method with plain set partitioning in hierarchical trees (SPIHT) technique showed that proposed method allows obtaining better result at image compression in terms of reconstruction quality and compression ratio.
RU
Актуальность проблемы повышения безопасности движения на железнодорожных переездах обусловлена увеличением количества автотранспортных средств и снижением дисциплины водителей. Одно из направлений для решения данной проблемы связано с использованием систем видеонаблюдения для мониторинга опасной зоны переезда. С учетом ограниченной полосы пропускания канала передачи данных в таких системах обычно применяется сжатие изображений. В данной работе представлен метод предварительной обработки для сжатия изображений. Предложенный метод учитывает неодинаковое субъективное информационное заполнение различных участков изображения переезда (используя нечеткую логику и вейвлет преобразование). Сравнение данного метода с простым методом пространственно упорядоченных иерархических деревьев (SPIHT) показало, что предложенный метод позволяет получить лучший результат при сжатии изображения с точки зрения качества восстановления и степени сжатия.
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In this paper we present experimental results comparing the quality of still Black & White (B/W) images compressed using four methods: JPEG, JPEG2000, EZW and SPIHT. The compression was performed on three pictures with differing levels of detail and density (bit-rates - bpp) using VCDemo software. The quality of the compressed pictures is determined by values of MSE, SNR and PSNR. The values are presented in appropriate tables and diagrams. By comparing the values obtained, we have found the methods that give best results depending on the picture bitrate and level of detail.
PL
W artykule opisano rezultaty eksperymentalnego badania kompresji obrazu czarno/białego przy wykorzystaniu czterech metod: JPEG, JPEG2000, EZW i SPIHT. Kompresję wykonywano na trzech obrazach o różnym poziomie detali i różnej gęstości.
W artykule przedstawiono metody kompresji obrazów cyfrowych, wykazując różnice technologiczne w różnych formatach. Wykonano analizę kompresji pliku wideo do różnych formatów wskazując zalety najnowszych technologii w porównaniu ze starszymi rozwiązaniami.
EN
In the paper a compression method of digital images showing the technology gap in different formats has been presented. An analysis of compressed video to various formats showing the adventages of the latest technologies in comparison with the older technologies has been presented, too.
W artykule przedstawiono wyniki prac realizowanych w ramach projektu VECTOR finansowanego w ramach 6 Programu Ramowego Unii Europejskiej w zakresie systemów kompresji i bezprzewodowej transmisji danych i obrazu dedykowanych dla endoskopii kapsułkowej nowej generacji. Opracowany system umożliwia transmisję 24 obrazów na sekundę o rozdzielczości 320x240 przez łącze radiowe o przepustowości 2 Mbit/s. Możliwość długotrwałej pracy kapsułki ma zapewnić układ zasilania bezprzewodowego zdolny dostarczyć do 300 mW mocy.
EN
The paper discusses the outcomes of the European Project VECTOR FP6 concerning image compression and wireless data transmission systems dedicated to a new generation of capsule endoscopy. The overview of the developed system is given in Section 2. The image compressor algorithm, developed within the project, is presented briefly in the first part of Section 3. Next, a new adaptive entropy encoder proposed for it is discussed in detail. The new encoder, in comparison to the previous one, has much lower memory footprint, as it does not use Huffman tables. Simulation results given in Tab. 1 clearly demonstrate that the new encoder, despite its low implementation complexity, outperform the old one. The developed image compressor algorithm was implemented in a single, 65 nm ultra low-power FPGA. It operates with 24 MHz clock and it is able to process a single image frame in 8.2 ms. Its energy consumption amounts to 0.4 mJ per single compressed frame. In Section 4 a wireless transmission system for wireless capsular endoscopy is presented. In this section an improved demodulator for a wireless receiver is also proposed. Section 5 contains the conclusions.
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This paper proposes an improved image coding scheme based on vector quantization. It is well known that the image quality of a VQ-compressed image is poor when a small-sized codebook is used. In order to solve this problem, the mean value of the image block is taken as an alternative block encoding rule to improve the image quality in the proposed scheme. To cut down the storage cost of compressed codes, a two-stage lossless coding approach including the linear prediction technique and the Huffman coding technique is employed in the proposed scheme. The results show that the proposed scheme achieves better image qualities than vector quantization while keeping low bit rates.
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In this paper the authors propose a new low-complexity approximation of 8-point discrete cosine transform (DCT) that requires 18 additions and two bit-shift operations. It is shown that the proposed transform outperforms significantly the known transform of the same computational complexity when applied to a JPEG compression stream in practical cases of encoding and decoding of still images. As such, the proposed transform can be effectively used in any practical applications where significant limitations exist regarding the computational capabilities coding and / or decoding devices, i.e. mobile devices or industrial imaging devices.
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In this paper a novel image compression method based on Block Truncation Coding technique is presented. The essence of this method is the designing of both, fixed and adaptive, multilevel quantizers and their appliance for discrete input. Experimentaly obtained results show that usage of compression based on this method provide lower bit rate values comparing to the previosly BTC-based results, while attaining requested level of quality.
PL
Zapropnowano nową metodę kompresji obrazu bazującą na technice BTC. Ideą tej metody jest zaprojektowanie wielopoziomowych kwantyzerów stałych i adaptacyjnych. Eksperymenty potwierdziły, że uzyskano lepszą przepływność bez pogorszenia jakości.
Experimental results of retained zone shape selection in zonal transform compression of greyscale images with the use of piecewise-linear transforms have been presented in the paper. The Walsh and PWL transforms used in the initial stage of the compression algorithm have been described. The zonal compression method, based on retaining of the spectral coefficients belonging to the specified zone, has been presented. Besides the commonly used square zone, various shapes of the retained spectral zone have been considered. Computational results have been compared in terms of the Mean-Square Error (MSE) and the Peak Signal-to-Noise Ratio (PSNR) versus the Compression Ratio (CR). Visual inspection of the reconstructed images has been performed. Results of experiments with the use of the described method have been enclosed in the form of plots and images.
PL
W artykule przedstawiono rezultaty badań nad wyborem kształtu zachowanej strefy w transformacyjnej kompresji strefowej obrazów ze skalą szarości z zastosowaniem transformat odcinkowo-liniowych. Opisano transformaty Walsha i PWL używane w początkowym stadium algorytmu kompresji. Przestawiono metodę kompresji strefowej, opartą o zachowywanie współczynników widmowych leżących wewnątrz wyspecyfikowanej strefy (rys. 2). Obok powszechnie stosowanej strefy kwadratowej rozważono różne kształty zachowanej strefy widmowej (rys. 3). Kształty stref dobrano na podstawie analizy obrazów widma współczynników stosowanych transformat (rys. 1). Przeanalizowano wpływ rozmiaru każdej z badanych stref na efektywność kompresji. W celu uniezależnienia analizy od parametrów wewnętrznych poszczególnych stref, określających bezpośrednio ich wymiary, wprowadzono obiektywny parametr, nazwany współczynnikiem kompresji (ang. Compression Ratio, CR), zależny wprost od liczby zer występujących w widmie zdegradowanym. Efektywność kompresji porównano w sensie błędu średniokwadratowego (ang. Mean-Square Error, MSE) oraz maksymalnego współczynnika sygnał-szum (ang. Peak Signal-to-Noise Ratio, PSNR) w funkcji współczynnika kompresji. Dokonano także wizualnej oceny jakości rekonstruowanych obrazów. Rezultaty badań z użyciem opisanej metody zamieszczono w postaci wykresów (rys. 4 - 7) i obrazów (rys. 8). Na podstawie przeprowadzonych badań wybrano strefy o najlepszym kształcie dla każdej z analizowanych transformat.
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A novel variable-sized block encoding with threshold control for colour image quantization (CIQ) is presented in this paper. In CIQ, the colour palette used has a great influence on the reconstructed image quality. Typically, a higher image quality and a larger storage cost are obtained when a larger-sized palette is used in CIQ. To cut down the storage cost while preserving quality of the reconstructed images, the threshold control policy for quadtree segmentation is used in this paper. Experimental results show that the proposed method adaptively provides desired bit rates while having better image qualities comparing to CIQ with the usage of multiple palettes of different sizes.
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In this paper, a novel greyscale image coding technique based on vector quantization (VQ) is proposed. In VQ, the reconstructed image quality is restricted by the codebook used in the image encoding/decoding procedures. To provide a better image quality using a fixed-sized codebook, the codebook expansion technique is introduced in the proposed technique. In addition, the block prediction technique and the relatively address technique are employed to cut down the required storage cost of the compressed codes. From the results, it is shown that the proposed technique adaptively provides better image quality at low bit rates than VQ.
In this paper a scheme of utilizing shape independent basis functions for the hierarchical multiresolution image compression is shown. For a given image texture region segmentation method is used. Following polygonal approximation of created segments causes a degradation of their boundaries. Using NURBS and Bezier interpolation and approximation segments' boundaries are created, thus achieving an image mask. As an input of the three-level hierarchical encoder this image mask and image are used. The image mask and image are subsampled by a factor of 2 on each level. The hierarchical encoder encodes them shape independently. Especially for a very low bit rate image coding gives better results for objective criteria (PSNR). For segment approximation the 2D shape independent orthogonal transform (DCT II) is used. Splines encoding and decoding is very efficient, because only their control points need to be stored. The segment is coded with a modified code similar to the JPEG code.
The paper presents a problem of reducing dimensionality of data structured in three-dimensional matrices, like true-color RGB digital images. In this paper we consider an application of Principal Component Analysis to one of the most typical image processing tasks, namely image compression. Unlike the cases reported in the literature [5,11,12] the compression being an application of three-dimensional PCA is performed on image blocks organized as three-dimensional structures (see Fig.1). In the first step, an image, which is stored as a three-dimensional matrix is decomposed into non-overlapping 3D blocks. Then each block is projected into lower-dimensional representation (1D or 2D) according to the chosen strategy: concatenation of rows, concatenation of columns, integration of rows, integration of columns [13] and concatenation of slices. Next, the blocks are centered (subtraction of mean value) and covariance matrices are being calculated. Finally, the eigenproblem is being solved on the covariance matrices giving a set of eigenvalues and eigenvectors, which are a base for creation of transformation matrices. Each block is then multiplied by respective transformation functions created from truncated eigenvectors matrices giving its reduced representation. The experimental part of the paper shows the comparison of strategies of calculating covariance matrices in the aspect of image reconstruction quality (evaluated by Peak Signal-to-Noise Ratio).
PL
W niniejszym artykule przedstawiono problem redukcji wymiarowości danych zorganizowanych w trójwymiarowych macierzach za pomocą metody Analizy Głównych Składowych (PCA). W przeciwieństwie do znanych metod prezentowanych w literaturze [5,11,12] wybrane metody opisane w pracy zakładają wykonanie obliczeń dla danych zagregowanych, bez ich rozdzielania na kanały. W pierwszym kroku algorytmu obraz kolorowy (macierz trójwymiarowa) jest dekomponowany na niezależne sub-bloki (3D). Następnie każdy z bloków jest poddawany projekcji 1D lub 2D zgodnie z przyjętą strategią: poprzez konkatenację wierszy, konkatenację kolumn, integrację wierszy, integracje kolumn lub konkatenację warstw. W kolejnym kroku są one centrowane i obliczane są odpowiednie macierze kowariancji. Następnie obliczany jest ich rozkład, który służy do stworzenia macierzy transformacji 3D PCA. Za ich pomocą przeprowadzana jest redukcja wymiarowości danych obrazowych. W przypadku omawianym w niniejszej pracy kompresji poddany jest obraz RGB i oceniana jest jakość rekonstrukcji (PSNR) jako funkcja liczby pozostawionych współczynników przekształcenia.
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