Single Image Dehazing Using Frequency Attention
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Discrete Cosine Transform (DCT) which has a major role in image and video compression has also a major role in power consumption. Approximate Computing let us trade precision to save power in error resilient applications such as multimedia. Therefore, DCT is a potential candidate for approximation. In this paper, we propose a method for evolutionary design of DCT architecture exploiting the inherent behavior of DCT. Unlike the prior works on DCT approximation, which concentrated mostly on optimizing, replacing, or removing less effective building blocks of DCT, in our proposed method we use the evolutionary method to find new structures for DCT. According to the results, the evolution methods lead to architectures with less area and acceptable accuracy.
Trellis quantization
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The performance of version I of the discrete cosine transform(DCT-I) is compared to version II of the discrete cosine transform (DCT-II) on various criteria. The results show that for a Markovian signal with correlation coefficient less than 0.8, the DCT-I performs as well as the DCT-II.
Lapped transform
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The discrete cosine transform (DCT) is widely applied in various fields, including image data compression, because it operates like the Karhunen-Loève transform for stationary random data. This paper presents a recursive algorithm for DCT with a structure that allows the generation of the next higher order DCT from two identical lower order DCT's. As a result, the method for implementing this recursive DCT requires fewer multipliers and adders than other DCT algorithms.
Discrete sine transform
Lapped transform
Trellis quantization
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DCT(Discrete Cosine Transform) 계수 제거 기법은 MC(Motion Compensated)-DCT 기반의 MPEG 비디오에서의 효율적인 율적응 트랜스코딩 기법이다. 그러나, 이들 기법에서는 DCT 계수 제거로 인한 왜곡이 전파되게 되고 종종 심각한 화질 열화를 유발하게 된다. 본 논문에서는 왜곡 전파 특성에 대한 두 가지의 통계적 성질을 제시하고 수식적으로 분석한다. 즉, 현재 프레임의 DCT 계수 제거 왜곡과 이전 프레임에서 전파되어 오는 왜곡간에 상관성이 없음을 보이고 각 프레임의 DCT 제거로 발생되는 전파 왜곡의 누적과 현 프레임의 DCT 계수 제거 왜곡의 합으로 전체 왜곡을 근사할 수 있음을 보인다. 시뮬레이션을 통하여, 본 논문에서 수식적으로 제시한 통계적 특성이 실제 비디오 시퀀스에서 유효함을 실험적으로 증명한다. Discrete cosine transform (DCT) coefficient dropping is well recognized as an efficient rate adaptation transcoding in motion-compensated (MC)-DCT based MPEG-compressed videos. However, in this scheme, the errors incurred by the DCT coefficient-dropping are propagated and often result in significant visual quality degradation. This paper presents two propositions describing well the statistical properties of propagated errors. That is, we propose that the DCT error of the current frame is not correlated to the propagated errors of the previous frames. We also propose that the overall distortions in a given frame can be approximated as the sum of the DCT error of the current frame and the propagated errors from the previous frames. Then, it is shown that several computer simulations with different video sequences verify the effectiveness of the proposed statistical analyses.
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Transcoding
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Trellis quantization
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Discrete Cosine Transform. Definitions and General Properties. DCT and Its Relations to the Karhunen-Loeve Transform. Fast Algorithms for DCT-II. Two Dimensional DCT Algorithms. Performance of the DCT. Applications of the DCT. Appendices. References. Index.
Discrete sine transform
Lapped transform
Trellis quantization
Discrete Hartley transform
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In the paper we discuss the problem of evaluating disc performance with benchmarks. In particular, we concentrate on assessing benchmark properties. For this purpose we have developed benchmark managing platform which allows us to enhance the benchmark execution process with monitoring performance counters. The developed methodology and tool do not need additional benchmark instrumentation and have a negligible impact on its execution. The usefulness of this approach has been illustrated with experimental results covering a representative set of benchmark programs.
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Convolutional neural networks use convolutional layers for feature extraction. Due to the limited feature extraction capabilities of traditional convolutional layers, the performance of convolutional neural network models is constrained. This paper presents an easy-to-port convolution module called Reticulated Convolution Module, which is dedicated to improving the ability of convolutional neural networks to extract key features. It adopts a funnel-like mesh structure, which first refines the features and then performs grouping, combination and fusion operations. We validate our Reticulated Convolution Module through extensive experiments on different types of datasets such as 101_food, Caltech-256 and GTSRB. The experimental results show that the convolutional neural network designed by Reticulated Convolution Module has excellent performance.
Convolution (computer science)
Feature (linguistics)
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The process of implementing monetary policy by the Central Bank is that of taking the advantage of the manipulative instruments to reach the goals of policy so the Central Bank should choose the manipulative instruments according to certain benchmark so as to reach its goals better. The benchmark of choosing instruments of monetary policy by the Central Bank include theoretical benchmark and empirical benchmark. The theoretical benchmark consist of external and internal benchmark. Internal benchmark are the most important benchmark for daily operation,which include initiative benchmark,fine-tuning benchmark,signal-functioning benchmark,timeliness benchmark,and operablity benchmark.
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