ZM-SPECK: A Fast and Memoryless Image Coder for Multimedia Sensor Networks

Kidwai, Naimur Rahman; Khan, E.; Reisslein, Martin

doi:10.1109/jsen.2016.2519600

Cited by 44 publications

(20 citation statements)

References 44 publications

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“…Although L-BCWT can reduce the complexity of repeated tree-scanning, bit-plane coding, and dynamic lists management of conventional 1D SPIHT, its compression performance remains marginal. ZM-SPECK [25] can remove state-maps and dynamic lists in the existing SPIHT algorithm using linear indexing property of wavelet tree and merged refinement technique. This method can reduce both computational complexity and memory accesses related to dynamic lists.…”

Section: Conventional Compression Algorithmsmentioning

confidence: 99%

“…Although the proposed compression method used only one additional line memory and slightly higher operation counts than the FALC method, it achieved better Table 4 Energy complexity of each compression algorithm (sum of microprocessor operation and memory accesses) Type SPIHT [18] L-BCWT [23] ZM-SPECK [25] VLC_NUQ [26] FALC [27] Proposed method 8L JPEG [40] 4L H.264 [13] 4L HEVC [15] #CPU operations (energy cost/frame)…”

Section: Comparison Of Power Consumption Changesmentioning

confidence: 99%

“…However, 1D SPIHT memory access counts are too high during the process of repetitively arranging key data in bit manipulations after wavelet transform for transmission, resulting in excessive power consumption. Several studies have been conducted to reduce memory accesses and improve compression ratio of 1D SPIHT [19][20][21][22], including line-based backward coding of wavelet trees (L-BCWT) [23,24] and zero memory set partitioned embedded block (ZM-SPECK) [25]. However, energy efficiency of systems after adopting these modified SPIHT methods needs to be further improved to satisfy energy requirements of low-power IoT devices.…”

Section: Introductionmentioning

confidence: 99%

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An energy-efficient low-memory image compression system for multimedia IoT products

Lee

Kim

2018

J Image Video Proc.

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Emerging Internet of things (IoT) technologies have rapidly expanded to multimedia applications, including highresolution image transmission. However, handling image data in IoT products with limited battery capacity requires low-complexity and small-size solutions such as low-memory compression techniques. The objective of this paper is to propose a line-based compression system based on four-level two-line discrete wavelet transform and adaptive line prediction. Bit stream is generated by multiplexing various frequency components with run-level coding combined with Huffman coding. The proposed system also includes a new bit rate control algorithm that could significantly improve image quality consistency in one frame. The proposed low-memory compression system can retain image quality for visually lossless compression criteria over the whole image frame. It can simultaneously lower total system power consumption in multimedia IoT products better than other existing low-memory compression techniques.

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Section: Conventional Compression Algorithmsmentioning

confidence: 99%

Section: Comparison Of Power Consumption Changesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An energy-efficient low-memory image compression system for multimedia IoT products

Lee

Kim

2018

J Image Video Proc.

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“…The difference between SPECK and SPIHT is that, SPECK does not use trees and spatial structure which span and exploit the similarity across different subbands of a wavelet decomposition; instead, it makes use of sets in the form of blocks of contiguous coefficients within subbands to exploit the clustering of energy in frequency and space in hierarchical structures of the transformed signal. In [10], a modified low-memory implementation of SPECK algorithm (ZM-SPECK) has proposed for quantization and coding of the DWT coefficients. The proposed method completely eliminates the linked lists and only use registers to perform low-level arithmetic/logical operations.…”

Section: Related Workmentioning

confidence: 99%

Optimal compression of vibration data with lifting wavelet transform and context-based arithmetic coding

Zhang

Hutchinson

Lieven

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-This paper proposes an adaptive vibration signal compression scheme composed of a lifting discrete wavelet transform (LDWT) with set-partitioning embedded blocks (SPECK) that efficiently sorts the wavelet coefficients by significance. The output of the SPECK module is input to an optimized context-based arithmetic coder that generates the compressed bitstream. The algorithm is deployed as part of a reliable and effective health monitoring technology for machines and civil constructions (e.g. power generation system). This application area relies on the collection of large quantities of high quality vibration sensor data that needs to be compressed before storing and transmission. Experimental results indicate that the proposed method outperforms state-of-the-art coders, while retaining the characteristics in the compressed vibration signals to ensure accurate event analysis. For the same quality level, up to 59.41% bitrate reduction is achieved by the proposed method compared to JPEG2000.

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“…(21,22) With MRA, the original signal was transferred by discrete wavelet decomposition (DWD) and decomposed into a low-frequency approximation (A1) and a high-frequency detail (D1). (23)(24)(25) Then, the first lowfrequency approximation (A1) is decomposed again into a high-frequency detail (D2) and a lowfrequency approximation (A2); similar decompositions can be obtained in accordance with the actual needs of wavelet decomposition layers as shown in Fig. 6.…”

Section: Feature Extraction By Dwt Decomposition For Emg Signalsmentioning

confidence: 99%

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

2017

Sensors and Materials

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In this study, a real-time electromyography (EMG)-triggered controller for a pneumatic artificial muscle (PAM) actuated lower limb rehabilitation robot is proposed. To make the rehabilitation task controllable by the patient's movement intention, a novel trigger controller is designed according to the EMG signals of the patient's muscle. For predicting his or her movement intention in advance, the EMG signals of the patient's muscle are captured and identified to realize the proposed EMG-triggered control. To guarantee the safety and performance of the proposed system, a patient's movement intention must be identified accurately by EMG feature extraction. First, the discrete wavelet transformation (DWT) technique is used to acquire the feature vectors of the EMG signals. The properties of the different feature spaces are taken into consideration, and the optimal multicomponents of features are chosen according to the experimental results. Second, support vector machines (SVMs) are studied to improve the classification performance. Finally, the MyRIO controller is used to implement a closed-loop control system for the rehabilitation robot with the movement-intention trigger control.

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ZM-SPECK: A Fast and Memoryless Image Coder for Multimedia Sensor Networks

Cited by 44 publications

References 44 publications

An energy-efficient low-memory image compression system for multimedia IoT products

An energy-efficient low-memory image compression system for multimedia IoT products

Optimal compression of vibration data with lifting wavelet transform and context-based arithmetic coding

Pneumatic Artificial Muscle Actuated Robot for Lower Limb Rehabilitation Triggered by Eelectromyography Signals Using Discrete Wavelet Transformation and Support Vector Machines

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