5 research outputs found
Проектирование процессора вычисления дискретного косинусного преобразования для систем сжатия изображения по схеме losless-to-lossy
Today, mobile multimedia systems that use the H.261 / 3/4/5, MPEG-1/2/4 and JPEG standards for encoding / decoding video, audio and images are widely spread [1–4]. The core of these standards is the discrete cosine transform (DCT) of I, II, III ... VIII types [DCT]. Wide support in a huge number of multimedia applications of the JPEG format by circuitry and software solutions and the need for image coding according to the L2L scheme determines the relevance of the problem of creating a decorrelated transformation based on DCT and methods for rapid prototyping of processors for computing an integer DCT on programmable systems on a FPGA chip. At the same time, such characteristics as structural regularity, modularity, high computational parallelism, low latency and power consumption are taken into account. Direct and inverse transformation should be carried out according to the “whole-to-whole” processing scheme with preservation of the perfective reconstruction of the original image (the coefficients are represented by integer or binary rational numbers; the number of multiplication operations is minimal, if possible, they are excluded from the algorithm). The wellknown integer DCTs (BinDCT, IntDCT) do not give a complete reversible bit to bit conversion. To encode an image according to the L2L scheme, the decorrelated transform must be reversible and implemented in integer arithmetic, i. e. the conversion would follow an “integer-to-integer” processing scheme with a minimum number of rounding operations affecting the compactness of energy in equivalent conversion subbands. This article shows how, on the basis of integer forward and inverse DCTs, to create a new universal architecture of decorrelated transform on FPGAs for transformational image coding systems that operate on the principle of “lossless-to-lossy” (L2L), and to obtain the best experimental results for objective and subjective performance compared to comparable compression systems.На сегодняшний день широко распространены мобильные мультимедийные системы, которые используют стандарты H.261/3/4/5, MPEG-1/2/4 и JPEG длякодирования/декодирования видео, аудио и изображений [1–4]. Ядром этих стандартов является дискретное косинусное преобразование (ДКП) I, II, III … VIII типов [ДКП]. Широкая поддержка в огромном количестве мультимедийных приложений формата JPEG схемотехническими и программными решениями и необходимость кодирования изображений по схеме L2L обусловливает актуальность проблемы создания декоррелирующего преобразования на основе ДКП и методов быстрого прототипирования процессоров вычисления целочисленного ДКП на программируемых системах на кристалле ПЛИС/FPGA. При этом во внимание принимаются такие характеристики, как структурная регулярность, модульность, высокий вычислительный параллелизм, малая латентность и потребляемая мощность. Прямое и обратное преобразования должны осуществляться по схеме обработки «целое к целому» с сохранением перфективной реконструкции исходного изображения (коэффициенты представляются целыми или двоичными рациональными числами; число операций умножения минимально, по возможности они исключаются из алгоритма). Известные целочисленные ДКП (BinDCT,IntDCT) не дают полного обратимого бит в бит преобразования. Для кодирования изображения по схеме L2L требуется, чтобы декоррелирующее преобразование было обратимым и реализовано в целочисленной арифметике, т. е. преобразование соответствовало бы схеме обработки «целое-в-целое» при минимальном числе операций округления, влияющих на компактность энергии в эквивалентных субполосах преобразования. В данной статье показано, как на основе целочисленного прямого и обратного ДКП создать новую универсальную архитектуру декоррелирующего преобразования на ПЛИС типа FPGA для систем трансформационного кодирования изображений, которые работают попринципу lossless-to-lossy (L2L), и получить лучшие экспериментальные результаты по объективным и субъективным показателям по сравнению с аналогичными системами сжатия
Архитектура процессора вычисления дискретного косинусного преобразования для систем сжатия изображения по схеме losless-to-lossy
The hardware implementations of fixed-point DCT blocks, known as IntDCT [1] and BinDCT [2], require some solutions. One of the main issues is the choice between the implementation of the conversion on FPGA, or the implementation on a digital signal processor (Digital Signal Processor, DSP). Each of the implementations has its own pros and cons. One of the most important advantages of the DSP implementation is the presence of special instructions used in DSP, in particular, the ability to multiply two numbers in one clock cycle. Therefore, with the advent of DSP, the limitation on the number of multiplications in algorithms was removed. On the other hand, when implementing a block on an FPGA, we can limit not ourselves to the bitness of the data (within reasonable limits), we have the ability to parallelize all incoming data and implement specialized computing cores for various tasks. In fact, designing multimedia systems on FPGAs reminds the design of similar systems based on the logic of a small and medium degree of integration. Such an implementation has the same limitations: a relatively small amount of available memory, the need to design basic structural elements (multipliers, divisors), etc. It is the inequality of the addition and multiplication operations when they are implemented on FPGAs that caused the search for DCT algorithms with the smallest number of factors. However, even this is not enough, since the structure of the multiplier is many times more complex than the structure of the adder, which made it necessary to look for ways to transform without using multiplications at all. This article shows how, on the basis of integer direct and inverse DCT and distributed arithmetic, to create a new universal architecture of decorrelated transform on FPGAs without multiplication operations for image transformation coding systems that operate on the principle of lossless-to-lossy (L2L), and to obtain the best experimental results in terms of hardware resources compared to comparable compression systems.Аппаратные реализации блоков дискретного косинусного преобразования (ДКП) на арифметике с фиксированной запятой, известные как IntDCT [1] и BinDCT [2], требуют решения некоторых вопросов. Один из главных вопросов – выбор между реализацией преобразования на ПЛИС или реализацией на цифровом сигнальном процессоре (Digital Signal Processor, DSP). Каждая из реализаций имеет как свои плюсы, так и минусы. Одним из самых главных достоинств реализации на DSP является наличие специальных инструкций, используемых в DSP, в частности, возможность перемножения двух чисел за один такт. Поэтому с появлением DSP было снято ограничение на количество умножений в алгоритмах. С другой стороны, при реализации блока на ПЛИС можно не ограничивать себя разрядностью данных (в разумных пределах), имеется возможность параллельной обработки всех поступающих данных и реализации специализированных вычислительных ядер для различных задач. По сути, проектирование систем мультимедиа на ПЛИС напоминает проектирование схожих систем на логике малой и средней степени интеграции. Такая реализация имеет те же ограничения: относительно малое количество доступной памяти, необходимость проектировать базовые элементы конструкции (умножители, делители) и т. д. Именно неравнозначность операций сложения и умножения при реализации их на ПЛИС и обусловила поиски алгоритмов ДКП с наименьшим числом множителей. Однако даже этого недостаточно, поскольку структура умножителя во много раз сложнее структуры сумматора, что заставило искать способы преобразования без использования умножений вообще. В статье показано, как на основе целочисленного прямого и обратного ДКП и распределенной арифметики создать новую универсальную архитектуру декоррелирующего преобразования на ПЛИС типа FPGA без операций умножения для систем трансформационного кодирования изображений, которые работают по принципу lossless-to-lossy (L2L), и получить лучшие экспериментальные результаты по аппаратным ресурсам по сравнению с аналогичными системами сжатия
Dual-DCT-Lifting-Based Lapped Transform with Improved Reversible Symmetric Extension
We present a lifting-based lapped transform (L-LT) and a reversible symmetric extension (RSE) in the boundary processing for more effective lossy-to-lossless image coding of data with various qualities from only one piece of lossless compressed data. The proposed dual-DCT-lifting-based LT (D2L-LT) parallel processes two identical LTs and consists of 1-D and 2-D DCT-liftings which allow the direct use of a DCT matrix in each lifting coefficient. Since the DCT-lifting can utilize any existing DCT software or hardware, it has great potential for elegant implementations that are dependent on the architecture and DCT algorithm used. In addition, we present an improved RSE (IRSE) that works by recalculating the boundary processing and solves the boundary problem that the DCT-lifting-based L-LT (DL-LT) has. We show that D2L-LT with IRSE mostly outperforms conventional L-LTs in lossy-to-lossless image coding
A novel 2D image compression algorithm based on two levels DWT and DCT transforms with enhanced minimize-matrix-size algorithm for high resolution structured light 3D surface reconstruction
Image compression techniques are widely used in 2D and 3D image and video sequences. There are many types of compression techniques and among the most popular are JPEG and JPEG2000. In this research, we introduce a new compression method based on applying a two level Discrete Wavelet Transform (DWT) and a two level Discrete Cosine Transform (DCT) in connection with novel compression steps for high-resolution images. The proposed image compression algorithm consists of 4 steps: 1) Transform an image by a two level DWT followed by a DCT to produce two matrices: DC- and AC-Matrix, or low and high frequency matrix respectively; 2) apply a second level DCT to the DC-Matrix to generate two arrays, namely nonzero-array and zero-array; 3) apply the Minimize-Matrix-Size (MMS) algorithm to the AC-Matrix and to the other high-frequencies generated by the second level DWT; 4) apply arithmetic coding to the output of previous steps. A novel Fast-Match-Search (FMS) decompression algorithm is used to reconstruct all high-frequency matrices. The FMS-algorithm computes all compressed data probabilities by using a table of data, and then using a binary search algorithm for finding decompressed data inside the table. Thereafter, all decoded DC-values with the decoded AC-coefficients are combined into one matrix followed by inverse two level DCT with two level DWT. The technique is tested by compression and reconstruction of 3D surface patches. Additionally, this technique is compared with JPEG and JPEG2000 algorithm through 2D and 3D RMSE following reconstruction. The results demonstrate that the proposed compression method has better visual properties than JPEG and JPEG2000 and is able to more accurately reconstruct surface patches in 3D
Novel methods of image compression for 3D reconstruction
Data compression techniques are widely used in the transmission and storage of 2D
image, video and 3D data structures. The thesis addresses two aspects of data
compression: 2D images and 3D structures by focusing research on solving the
problem of compressing structured light images for 3D reconstruction. It is useful then
to describe the research by separating the compression of 2D images from the
compression of 3D data. Concerning image compression, there are many types of
techniques and among the most popular are JPEG and JPEG2000. The thesis
addresses different types of discrete transformations (DWT, DCT and DST)
thatcombined in particular ways followed by Matrix Minimization algorithm,which is
achieved high compression ratio by converting groups of data into a single value. This
is an essential step to achieve higher compression ratios reaches to 99%. It is
demonstrated that the approach is superior to both JPEG and JPEG2000 for
compressing 2D images used in 3D reconstruction. The approach has also been tested
oncompressing natural or generic 2D images mainly through DCT followed by Matrix
Minimization and arithmetic coding.Results show that the method is superior to JPEG
in terms of compression ratios and image quality, and equivalent to JPEG2000 in
terms of image quality.
Concerning the compression of 3D data structures, the Matrix Minimization algorithm
is used to compress geometry and connectivity represented by a list of vertices and a
list of triangulated faces. It is demonstrated that the method can compress vertices
very efficiently compared with other 3D formats. Here the Matrix Minimization
algorithm converts each vertex (X, Y and Z) into a single value without the use of any
prior discrete transformation (as used in 2D images) and without using any coding
algorithm. Concerningconnectivity,the triangulated face data are also compressed with
the Matrix Minimizationalgorithm followed by arithmetic coding yielding a stream of
compressed data. Results show compression ratiosclose to 95% which are far superior
to compression with other 3D techniques.
The compression methods presented in this thesis are defined as per-file compression.
The methods to generate compression keys depend on the data to be compressed.
Thus, each file generates their own set of compression keys and their own set of
unique data. This feature enables application in the security domain for safe
transmission and storage of data. The generated keys together with the set of unique
data can be defined as an encryption key for the file as, without this information, the
file cannot be decompressed