A Block-Based Arithmetic Entropy Encoding Scheme for Medical Images

Sharma, Urvashi; Sood, Meenakshi; Puthooran, Emjee; Kumar, Yugal

doi:10.4018/ijhisi.2020070104

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…The Arithmetic approach is used for image compression and secure biometric applications. In [43], the authors introduce a novel image compression technique using block-based arithmetic encoding to select the threshold for prediction and choose the optimal block size of the residual image. Their approach outperforms other lossless image compression techniques, such as CALIC, JPEG-LS, HEVC and 3D-MRP.…”

Section: Arithmetic Encodingmentioning

confidence: 99%

Optimizing Real-Time ECG Data Transmission in Constrained Environments

Ouda

Badr

Rashwan

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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ECG monitoring systems have a significant role in detecting cardiovascular diseases and reducing the rate of sudden cardiac deaths through early warnings for heart attacks. One of the critical factors in supporting real-time ECG tracking is to guarantee monitoring system availability. This thesis focuses on battery life extension for a 12 Lead ECG patch utilizing compression and classification approaches. the opportunity to explore students' activities at Queen's while applying work-life balance.I would also like to thank all TRL and IoT Lab colleagues, partners of success, for their great support and encouragement towards making a successful and enjoyable academic experience. v

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Section: Arithmetic Encodingmentioning

confidence: 99%

Optimizing Real-Time ECG Data Transmission in Constrained Environments

Ouda

Badr

Rashwan

et al. 2022

ICC 2022 - IEEE International Conference on Communications

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“…The codeword length is almost proportional to the probability logarithm. The most widely used symbols therefore take shorter codes [17].…”

Section: Lossless Techniquesmentioning

confidence: 99%

Image Compression Using Neural Networks: A Review

et al. 2021

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Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

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“…In different image regions, there are different error probabilities so the quantized prediction error image obtained after RIGED is divided into blocks before applying arithmetic encoder. Image segmentation is done in fixed size blocks of 8 × 8 [22], as it is optimal block size obtained after empirical analysis providing minimum bit rate of compressed image. Classification of blocks is done into different groups on the mean absolute error basis.…”

Section: Block Adaptive Arithmetic Encodingmentioning

confidence: 99%

“…Contribution Addressing the previous stated problems, this research work contributes a novel effective near-lossless coding method providing high coding efficiency. The proposed near-lossless method is based on the predictive coding method employing RIGED with optimal threshold value [20,21] for prediction, quantizer with optimal q level, and BAAE [22] with optimal block size for encoding. RIGED provides efficient prediction of the image results in less entropy valued residual image and BAAE offers optimal block size for encoding.…”

Section: Introductionmentioning

confidence: 99%

A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment

2019

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The near-lossless compression technique has better compression ratio than lossless compression technique while maintaining a maximum error limit for each pixel. It takes the advantage of both the lossy and lossless compression methods providing high compression ratio, which can be used for medical images while preserving diagnostic information. The proposed algorithm uses a resolution and modality independent threshold-based predictor, optimal quantization (q) level, and adaptive block size encoding. The proposed method employs resolution independent gradient edge detector (RIGED) for removing inter-pixel redundancy and block adaptive arithmetic encoding (BAAE) is used after quantization to remove coding redundancy. Quantizer with an optimum q level is used to implement the proposed method for high compression efficiency and for the better quality of the recovered images. The proposed method is implemented on volumetric 8-bit and 16-bit standard medical images and also validated on real time 16-bit-depth images collected from government hospitals. The results show the proposed algorithm yields a high coding performance with BPP of 1.37 and produces high peak signal-to-noise ratio (PSNR) of 51.35 dB for 8-bit-depth image dataset as compared with other near-lossless compression. The average BPP values of 3.411 and 2.609 are obtained by the proposed technique for 16-bit standard medical image dataset and real-time medical dataset respectively with maintained image quality. The improved near-lossless predictive coding technique achieves high compression ratio without losing diagnostic information from the image.

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A Block-Based Arithmetic Entropy Encoding Scheme for Medical Images

Cited by 5 publications

References 14 publications

Optimizing Real-Time ECG Data Transmission in Constrained Environments

Optimizing Real-Time ECG Data Transmission in Constrained Environments

Image Compression Using Neural Networks: A Review

A Block Adaptive Near-Lossless Compression Algorithm for Medical Image Sequences and Diagnostic Quality Assessment

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