Fast seismic data compression based on high‐efficiency SPIHT

Xie, Kai; Bai, Zongqing; Yu, WenMao

doi:10.1049/el.2013.3834

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…The popularity of the wavelet based coding scheme could be found in its efficient data representation in the transformed domain which easily allows compressed image manipulation, e.g., by utilizing straightforward quality control scheme or progressive image decompression. One such effective coding scheme, based on set partitioning in hierarchical trees (SPIHT), was recently adopted to seismic data in [10], and also partially adopted by methods in [5].…”

Section: A Related Work On Lossy Seismic Data Compressionmentioning

confidence: 99%

“…To reach a solution that will not require look-up table, and thus relaxing memory requirements, we compare our newly obtained λ value with previous ones given by (10). We propose to compensate the large values of λ old when Q p > 51 and hence provide better compromise between D and R. We refer λ after we apply compensation as λ new , and we assume that it is still in the form of k * 2…”

Section: G Rate-distortion Optimization -A New Model For the Lagrangmentioning

confidence: 99%

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High Bit-Depth Seismic Data Compression: A Novel Codec Under the Framework of HEVC

Radosavljević

Xiong

et al. 2020

IEEE Access

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M. Radosavljević and D. Vukobratović would like to acknowledge the European Union's Horizon 2020 research and innovation project under Grant Agreement number 856697. ABSTRACT Motivated by the superior performance of High Efficiency Video Coding (HEVC), and driven by the rapid growth in data volume produced by seismic surveys, in this work we explore a 32 bits per pixel (b/p) extension of the HEVC codec for compression of seismic data. We propose to reassemble seismic slices in a format that corresponds to video signal and benefit from the coding gain achieved by HEVC inter mode, besides the possible advantages of the (still image) HEVC intra mode. To this end, we modify almost all components of the original HEVC codec to cater for high bit-depth coding of seismic data: Lagrange multiplier used in optimization of the coding parameters has been adapted to the new data statistics, core transform and quantization have been reimplemented to handle the increased bit-depth range, and modified adaptive binary arithmetic coder has been employed for efficient entropy coding. Even though the new codec after implementation of the proposed modifications goes beyond the standardized HEVC, it still maintains a generic HEVC structure, and it is developed under the general HEVC framework. Thus, we tailored a specific codec design which, when compared to the JPEG-XR and commercial wavelet-based codec, significantly improves the peak-signal-to-noise-ratio (PSNR) vs. compression ratio performance for 32 b/p seismic data. Depending on a configuration, PSNR gain goes from 3.39 dB up to 9.48 dB. Also, relying on the specific characteristics of seismic data, we proposed an optimized encoder that reduces encoding time by 67.17% for All-I configuration on trace image dataset, and 67.39% for All-I, 97.96% for P2-configuration and 98.64% for B-configuration on 3D wavefield dataset, with negligible coding performance losses. INDEX TERMS High bit-depth seismic data compression, 3D volumetric seismic data, HEVC.

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Section: A Related Work On Lossy Seismic Data Compressionmentioning

confidence: 99%

Section: G Rate-distortion Optimization -A New Model For the Lagrangmentioning

confidence: 99%

High Bit-Depth Seismic Data Compression: A Novel Codec Under the Framework of HEVC

Radosavljević

Xiong

et al. 2020

IEEE Access

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“…With the increasing pace of people’s exploration of the objective world, the 3D seismic exploration technology is also gradually in-depth, but if the SNR of 3D seismic signal is low, it will affect the follow-up signal processing and interpretation work, which may lead to a large miscalculation. Therefore, it is of practical significance to study efficient 3D seismic signal denoising algorithm [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Chen

Xie

Wen

et al. 2020

Sensors

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In order to enhance weak signals in strong noise background, a weak signal enhancement method based on EMDNN (neural network-assisted empirical mode decomposition) is proposed. This method combines CEEMD (complementary ensemble empirical mode decomposition), GAN (generative adversarial networks) and LSTM (long short-term memory), it enhances the efficiency of selecting effective natural mode components in empirical mode decomposition, thus the SNR (signal-noise ratio) is improved. It can also reconstruct and enhance weak signals. The experimental results show that the SNR of this method is improved from 4.1 to 6.2, and the weak signal is clearly recovered.

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“…This paper proposes a new image preprocessing method named GBS and a medical invoice recognition algorithm which combines CNN and RNN together, in order to recognize medical invoices, which greatly improves the performance of medical invoice recognition, after having studied the existing achievements in signal processing and deep learning in our laboratory [50,51,52]. In the algorithm combining the Alexnet with Adam optimization algorithm, using the Adam optimization algorithm can make the network convergence fast and reduce the loss of the advantages of network training, greatly improving the recognition rate.…”

Section: Introductionmentioning

confidence: 99%

Dual Model Medical Invoices Recognition

Zhao

Sheng

et al. 2019

Sensors

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Hospitals need to invest a lot of manpower to manually input the contents of medical invoices (nearly 300,000,000 medical invoices a year) into the medical system. In order to help the hospital save money and stabilize work efficiency, this paper designed a system to complete the complicated work using a Gaussian blur and smoothing–convolutional neural network combined with a recurrent neural network (GBS-CR) method. Gaussian blur and smoothing (GBS) is a novel preprocessing method that can fix the breakpoint font in medical invoices. The combination of convolutional neural network (CNN) and recurrent neural network (RNN) was used to raise the recognition rate of the breakpoint font in medical invoices. RNN was designed to be the semantic revision module. In the aspect of image preprocessing, Gaussian blur and smoothing were used to fix the breakpoint font. In the period of making the self-built dataset, a certain proportion of the breakpoint font (the font of breakpoint is 3, the original font is 7) was added, in this paper, so as to optimize the Alexnet–Adam–CNN (AA-CNN) model, which is more suitable for the recognition of the breakpoint font than the traditional CNN model. In terms of the identification methods, we not only adopted the optimized AA-CNN for identification, but also combined RNN to carry out the semantic revisions of the identified results of CNN, meanwhile further improving the recognition rate of the medical invoices. The experimental results show that compared with the state-of-art invoice recognition method, the method presented in this paper has an average increase of 10 to 15 percentage points in recognition rate.

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Fast seismic data compression based on high‐efficiency SPIHT

Cited by 13 publications

References 5 publications

High Bit-Depth Seismic Data Compression: A Novel Codec Under the Framework of HEVC

High Bit-Depth Seismic Data Compression: A Novel Codec Under the Framework of HEVC

Weak Signal Enhance Based on the Neural Network Assisted Empirical Mode Decomposition

Dual Model Medical Invoices Recognition

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