56 research outputs found
Deterministische und stochastische Rundungsfehleranalysen von schnellen trigonometrischen Algorithmen in Gleitkomma- bzw. Festkomma-Arithmetik
In dieser Dissertation wird eine umfassende und einheitliche Stabilitätsanalyse sowohl in Festkomma- als auch in Gleitkomma-Arithmetik für eine Klasse von schnellen DCT- und DST-Algorithmen durchgeführt, welche auf Faktorisierungen der orthogonalen Transformationsmatrizen in Produkte von dünnbesetzten orthogonalen Matrizen beruhen. Neben Untersuchungen für den ungünstigsten Fall (worst case) wird auch jeweils eine stochastische Rundungsfehleranalyse (average case) durchgeführt, welche ohne die in der Bildverarbeitung selten gegebene Unkorreliertheit der Eingangsdaten auskommt.In this thesis, a comprehensive and unified stability analysis for a class of fast DCT (discrete cosine transform) and DST (discrete sine transform) algorithms is performed, both for fixed-point and floating-point arithmetic. Each of them is based on a factorization of the underlying orthogonal transform matrix into a product of sparse orthogonal matrices. Additionally to worst case analysis, also the average case is considered using stochastic models for the relative and absolute roundoff errors. Particularly with regard to applications in digital image processing, the stochastic analysis of roundoff error is done without assuming the data to be uncorrelated or independent
Joint Optimization of Low-power DCT Architecture and Effcient Quantization Technique for Embedded Image Compression
International audienceThe Discrete Cosine Transform (DCT)-based image com- pression is widely used in today's communication systems. Signi cant research devoted to this domain has demonstrated that the optical com- pression methods can o er a higher speed but su er from bad image quality and a growing complexity. To meet the challenges of higher im- age quality and high speed processing, in this chapter, we present a joint system for DCT-based image compression by combining a VLSI archi- tecture of the DCT algorithm and an e cient quantization technique. Our approach is, rstly, based on a new granularity method in order to take advantage of the adjacent pixel correlation of the input blocks and to improve the visual quality of the reconstructed image. Second, a new architecture based on the Canonical Signed Digit and a novel Common Subexpression Elimination technique is proposed to replace the constant multipliers. Finally, a recon gurable quantization method is presented to e ectively save the computational complexity. Experimental results obtained with a prototype based on FPGA implementation and com- parisons with existing works corroborate the validity of the proposed optimizations in terms of power reduction, speed increase, silicon area saving and PSNR improvement
Orthonormal and biorthonormal filter banks as convolvers, and convolutional coding gain
Convolution theorems for filter bank transformers are introduced. Both uniform and nonuniform decimation ratios are considered, and orthonormal as well as biorthonormal cases are addressed. All the theorems are such that the original convolution reduces to a sum of shorter, decoupled convolutions in the subbands. That is, there is no need to have cross convolution between subbands. For the orthonormal case, expressions for optimal bit allocation and the optimized coding gain are derived. The contribution to coding gain comes partly from the nonuniformity of the signal spectrum and partly from nonuniformity of the filter spectrum. With one of the convolved sequences taken to be the unit pulse function,,e coding gain expressions reduce to those for traditional subband and transform coding. The filter-bank convolver has about the same computational complexity as a traditional convolver, if the analysis bank has small complexity compared to the convolution itself
Wordlength optimization for linear digital signal processing
Published versio
Realtime image noise reduction FPGA implementation with edge detection
The purpose of this dissertation was to develop and implement, in a Field
Programmable Gate Array (FPGA), a noise reduction algorithm for real-time
sensor acquired images. A Moving Average filter was chosen due to its
fulfillment of a low demanding computational expenditure nature, speed, good
precision and low to medium hardware resources utilization. The technique is
simple to implement, however, if all pixels are indiscriminately filtered, the result
will be a blurry image which is undesirable.
Since human eye is more sensitive to contrasts, a technique was
introduced to preserve sharp contour transitions which, in the author’s opinion,
is the dissertation contribution. Synthetic and real images were tested.
Synthetic, composed both with sharp and soft tone transitions, were generated
with a developed algorithm, while real images were captured with an 8-kbit
(8192 shades) high resolution sensor scaled up to 10 × 103 shades.
A least-squares polynomial data smoothing filter, Savitzky-Golay, was
used as comparison. It can be adjusted using 3 degrees of freedom ─ the
window frame length which varies the filtering relation size between pixels’
neighborhood, the derivative order, which varies the curviness and the
polynomial coefficients which change the adaptability of the curve. Moving
Average filter only permits one degree of freedom, the window frame length.
Tests revealed promising results with 2 and 4ℎ polynomial orders. Higher
qualitative results were achieved with Savitzky-Golay’s better signal
characteristics preservation, especially at high frequencies.
FPGA algorithms were implemented in 64-bit integer registers serving
two purposes: increase precision, hence, reducing the error comparatively as if
it were done in floating-point registers; accommodate the registers’ growing
cumulative multiplications. Results were then compared with MATLAB’s double
precision 64-bit floating-point computations to verify the error difference
between both. Used comparison parameters were Mean Squared Error, Signalto-Noise Ratio and Similarity coefficient.O objetivo desta dissertação foi desenvolver e implementar, em FPGA,
um algoritmo de redução de ruído para imagens adquiridas em tempo real.
Optou-se por um filtro de Média Deslizante por não exigir uma elevada
complexidade computacional, ser rápido, ter boa precisão e requerer moderada
utilização de recursos. A técnica é simples, mas se abordada como filtragem
monotónica, o resultado é uma indesejável imagem desfocada.
Dado o olho humano ser mais sensível ao contraste, introduziu-se uma
técnica para preservar os contornos que, na opinião do autor, é a sua principal
contribuição. Utilizaram-se imagens sintéticas e reais nos testes. As sintéticas,
compostas por fortes e suaves contrastes foram geradas por um algoritmo
desenvolvido. As reais foram capturadas com um sensor de alta resolução de
8-kbit (8192 tons) e escalonadas a 10 × 103 tons.
Um filtro com suavização polinomial de mínimos quadrados, SavitzkyGolay, foi usado como comparação. Possui 3 graus de liberdade: o tamanho da
janela, que varia o tamanho da relação de filtragem entre os pixels vizinhos; a
ordem da derivada, que varia a curvatura do filtro e os coeficientes polinomiais,
que variam a adaptabilidade da curva aos pontos a suavizar. O filtro de Média
Deslizante é apenas ajustável no tamanho da janela. Os testes revelaram-se
promissores nas 2ª e 4ª ordens polinomiais. Obtiveram-se resultados
qualitativos com o filtro Savitzky-Golay que detém melhores características na
preservação do sinal, especialmente em altas frequências.
Os algoritmos em FPGA foram implementados em registos de vírgula
fixa de 64-bits, servindo dois propósitos: aumentar a precisão, reduzindo o erro
comparativamente ao terem sido em vírgula flutuante; acomodar o efeito
cumulativo das multiplicações. Os resultados foram comparados com os
cálculos de 64-bits obtidos pelo MATLAB para verificar a diferença de erro
entre ambos. Os parâmetros de medida foram MSE, SNR e coeficiente de
Semelhança
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