Improved Efficiency on Adaptive Arithmetic Coding for Data Compression Using Range-Adjusting Scheme, Increasingly Adjusting Step, and Mutual-Learning Scheme

Ding, Jian-Jiun; Wang, I-Hsiang; Chen, Hung-Yi

doi:10.1109/tcsvt.2017.2749449

Cited by 18 publications

(5 citation statements)

References 22 publications

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“…With the addition of semantic labels, there is a large difference in the amount of coding between the region of interest and the background region, which may lead to the lack of authenticity of the compressed image. We improve formula (9) and added the energy function of MRF to the loss function on the basis of GAN loss function. With the addition of the energy function, the pixels of the region of interest maintain continuity with the pixels of the region of background in image features at the maximum probability.…”

Section: High Fidelity Image Compression Of Uav Based On Based Omentioning

confidence: 99%

“…The first compression coding stage is mainly used to remove redundancy, such as PCM [7] and transformation coding method [8]. The emergence of arithmetic coding [9] marks the beginning of the second generation of image compression coding, followed by dictionary coding [10] and lossless compression algorithm. The third generation compression technology mainly includes fractal image, wavelet transform [11] and other coding algorithms, and has been developing till now.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

et al. 2019

IEEE Access

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This paper proposes an improved image high fidelity compression algorithm based on the generative adversarial networks (GANs) to deal with the problem that the UAV image has a large amount of data which is not conducive to post-processing. By adding an encoder in front of the generator, the disaster area image transmitted by UAV is compressed to meet the requirements of the generator. After the compressed image is trained together with the real image through the discriminator, the quality of the compressed image is constantly improved. This image compression algorithm can fully synthesize the codes of non-major areas such as trees and rivers in the image, and try to retain the codes of important areas such as houses and roads. The experimental results show that the proposed compression method in this paper has a higher compression ratio than the traditional compression method for the disaster area image, and can obtain images with strong sense of hierarchy. INDEX TERMS Complex disaster, convolutional neural networks, discriminator, generative adversarial networks, generator, high fidelity, image compression.

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Section: High Fidelity Image Compression Of Uav Based On Based Omentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

et al. 2019

IEEE Access

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“…Currently, there are several directions of lossless compression algorithm development, including lossless algorithm improvement, data encryption, and lossless deep learning feature compression [21]- [23]. Compression algorithm such as the arithmetic coding method can be improved by using the range adjusting, step size adjusting, and mutual learning scheme [21], where the compression efficiency is mainly focused. In [22], lossless compression algorithm involving with data encryption for high capacity data hiding is proposed.…”

Section: Introductionmentioning

confidence: 99%

2-D Bi-Level Block Coding for Color Image Compression and Transmission With Bit-Error Awareness

et al. 2020

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In this paper, we develop a new color image lossless compression algorithm with bit-error awareness based on a general bi-level block coding method. The proposed method contains three stages. First, a color image in the RGB color space is converted to the YCrCb color space by lossless reversible transformations. Next, predictors in the YCrCb color space are applied to generate the residue sequences. In order to achieve a better image compression ratio, a particle swarm optimization (PSO) algorithm is adopted to search the best combination from the candidates of color transformations and predictors which generate the minimum residue entropy. Third, a new 2-D bi-level block coding is developed to further encode the residue sequences. Comparing with the existing residue coding methods, including 1-D bi-level block coding, interval Huffman coding, and standard Huffman coding, the 2-D bi-level block coding algorithm can improve image compression ratio as well as preserving the bit-error resilience. Finally, the key parameters such as color transformation information, predictor parameters and residue coding parameters are protected using (7, 4) Hamming code in the bit stream before transmission. The performances are validated in terms of compression ratio (CR) and peak signal to noise ratio (PSNR). The compression algorithm with bi-level coding achieves the highest PSNR when the bit-error rate (BER) is larger than 0.001 and maintains an acceptable PSNR for BER less than 0.001. In particular, the compression algorithm using the new 2-D bi-level block coding scheme achieves the highest CR. INDEX TERMS lossless compression, bit-error awareness, general bi-level block coding, particle swarm optimization, (7, 4) Hamming code.

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“…The dynamic kth-order unary/Exp-Golomb rice coding is applied to accommodate the large-valued prediction residues [15]. Reference [16] improves efficiency on context-based adaptive arithmetic coding (CAAC) by making thefrequency table (the table used to estimate the probability distribution of data according to the past input) of CAAC converge to the true probability distribution. Basically, the various entropy coders described above from the hybrid video coding framework primarily deal with the statistical redundancy of the transform coefficients.…”

Section: Introductionmentioning

confidence: 99%

A Lossless Recompression Approach for Video Streaming Transmission

Wang

Hou

et al. 2019

IEEE Access

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With the increasing amount of HD or even UHD video, the video streaming transmission over the mobile network is confronted with various quality of experience issues. To facilitate the delivery of compressed video over the network, it is extremely helpful to further compress the coded video bit stream without any information loss, so-called lossless recompression. However, the existing lossless video/image compression algorithms aim to remove the pixel-domain (e.g., lossless coding mode of HEVC) or the coefficient-domain (e.g., entropy coding) redundancy rather than the bit-stream-domain redundancy. To this end, we propose a novel lossless recompression approach to eliminate statistical redundancy in the coded video bit stream. The core idea is to increase the probability of this repetitive pattern of continuous ''0'' or ''1'' symbol strings in the binary symbol sequence by rearranging the sequence, including value mapping and position aggregation. In particular, Fibonacci-based mapping rule is used to convert the original sequence (those binary symbols in Fibonacci positions) into a new one with more continuous ''0'' or ''1.'' Besides, we aggregate as many identical symbols as possible together to further increase the probability of the occurrence of repetitive patterns. Finally, the lossless compression algorithm and the asymptotic lossless compression scheme are designed to achieve a compact representation of the rearranged symbol sequence. We also regulate formal syntactic structures for the proposed mapping rule, aggregation algorithm, and recompression scheme so as to deterministically revert from the recoded version to the original bit stream. The experimental results on compressed H.264/AVC and H.265/HEVC video data show that our approach can considerably reduce the bit rate of streaming video. Video streaming, video coding, lossless compression, recompression, Fibonacci. INDEX TERMS

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Improved Efficiency on Adaptive Arithmetic Coding for Data Compression Using Range-Adjusting Scheme, Increasingly Adjusting Step, and Mutual-Learning Scheme

Cited by 18 publications

References 22 publications

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

UAV Image High Fidelity Compression Algorithm Based on Generative Adversarial Networks Under Complex Disaster Conditions

2-D Bi-Level Block Coding for Color Image Compression and Transmission With Bit-Error Awareness

A Lossless Recompression Approach for Video Streaming Transmission

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