Compression of geometry videos by 3D-SPECK wavelet coder

Bahce, Canan Gulbak; Bayazıt, Uluğ

doi:10.1007/s00371-020-01847-z

Cited by 6 publications

(3 citation statements)

References 43 publications

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“…Wavelets ( Daubechies, 1992 ) are powerful analysis tools used in the fields of data denoising ( Donoho, 1995 ; Donoho and Johnstone, 1994 ), image compression ( Bahce and Bayazit, 2020 ; Kim and Pearlman, 1997 ; Wu et al , 2009 ), etc., and 2D wavelets have been introduced into deep learning ( Duan et al , 2017 ; Guan et al , 2019 ; Huang et al , 2017 ; Liu et al , 2018a , 2020a ,b; Shi and Pun, 2017 ; Williams and Li, 2018 ; Yang et al , 2019 ; Yoo et al , 2019 ). Alike 2D wavelets in deep learning, 3D wavelets are applied as pre-processing or post-processing tools in deep networks.…”

Section: Methodsmentioning

confidence: 99%

Neuron segmentation using 3D wavelet integrated encoder–decoder network

Shen

2021

Bioinformatics

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Motivation 3D neuron segmentation is a key step for the neuron digital reconstruction, which is essential for exploring brain circuits and understanding brain functions. However, the fine line-shaped nerve fibers of neuron could spread in a large region, which brings great computational cost to the neuron segmentation. Meanwhile, the strong noises and disconnected nerve fibers bring great challenges to the task. Results In this paper, we propose a 3D wavelet and deep learning based 3D neuron segmentation method. The neuronal image is first partitioned into neuronal cubes to simplify the segmentation task. Then, we design 3D WaveUNet, the first 3D wavelet integrated encoder-decoder network, to segment the nerve fibers in the cubes; the wavelets could assist the deep networks in suppressing data noises and connecting the broken fibers. We also produce a Neuronal Cube Dataset (NeuCuDa) using the biggest available annotated neuronal image dataset, BigNeuron, to train 3D WaveUNet. Finally, the nerve fibers segmented in cubes are assembled to generate the complete neuron, which is digitally reconstructed using an available automatic tracing algorithm. The experimental results show that our neuron segmentation method could completely extract the target neuron in noisy neuronal images. The integrated 3D wavelets can efficiently improve the performance of 3D neuron segmentation and reconstruction. Availability The data and codes for this work are available at https://github.com/LiQiufu/3D-WaveUNet. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

Neuron segmentation using 3D wavelet integrated encoder–decoder network

Shen

2021

Bioinformatics

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“…Therefore, only those features should be used that efficiently represent the original data. Bahce in [30], proposed a 3D-SPECK method for the encoding of geometry videos.…”

Section: Related Workmentioning

confidence: 99%

Fast Intra Mode Selection in HEVC Using Statistical Model

Tariq¹,

Alfalou²,

Ijaz³

et al. 2022

Computers, Materials &Amp; Continua

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Comprehension algorithms like High Efficiency Video Coding (HEVC) facilitates fast and efficient handling of multimedia contents. Such algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing quality. However, the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia content. Therefore, a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the block. Normally, the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost (rate-distortion). Firstly, the proposed work utilizes neighboring blocks to extract available information for the current block. Then this information is converted to the probability that tells which intra mode might be best in the current situation. The proposed model has a strong foundation as it is based on the probability rule-2 which says that the sum of probabilities of all outcomes should be 1. Moreover, it is also based on optimal stopping theory (OST). Therefore, the proposed model performs better than many existing OST and classical secretary-based models. The proposed algorithms expedited the encoding process by 30.22% of the HEVC with 1.35% Bjontegaard Delta Bit Rate (BD-BR).

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“…Therefore, features should only be used that efficiently represent the original data. Bahce, in [31], proposed a 3D-SPECK method for the encoding of geometry videos. Jridi in [32,33] proposed an architecture for Discrete Cosine Transform (DCT) to reduce the complexity of HEVC.…”

Section: Related Workmentioning

confidence: 99%

HEVC Fast Intra-Mode Selection Using World War II Technique

et al. 2021

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High-Efficiency Video Coding (HEVC) applies 35 intra modes to every block of a frame and selects the mode that gives the best prediction. This brute-force nature of HEVC makes it complex and unfit for real-time applications. Therefore, a fast intra-mode estimation algorithm is presented here based on the classic World War II (WW2) technique known as the ‘German Tanks Problem’ (GTP). This not only is the first article to use GTP for early estimation of intra mode, but also expedites the estimation process of GTP. Secondly, the various elements of the intra process are efficiently mapped to the elements of GTP estimation. Finally, the two variations of GPT are modeled and are also minimum-variance estimates. These experimental results indicate that proposed GTP-based fast estimation reduced the compression time of HEVC from 23.88% to 31.44%.

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Compression of geometry videos by 3D-SPECK wavelet coder

Cited by 6 publications

References 43 publications

Neuron segmentation using 3D wavelet integrated encoder–decoder network

Neuron segmentation using 3D wavelet integrated encoder–decoder network

Fast Intra Mode Selection in HEVC Using Statistical Model

HEVC Fast Intra-Mode Selection Using World War II Technique

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