Abstract
Emerging Internet of things (IoT) technologies have rapidly expanded to multimedia applications, including high-resolution 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.
Highlights
With exciting development of very large-scale integration (VLSI) technology, Internet of things (IoT) products have rapidly evolved into monitoring devices that can capture and stream multimedia data with high-resolution images
This method can estimate the power consumption of an algorithm operating on a target embedded system while avoiding significant estimation deviation due to many complicated interactions among the embedded program implementation techniques, the optimization level, and the operating system (OS) programs [35]
To assess power reduction performance of existing IoT systems, we assumed that the evaluation system had specifications similar to those of Intel Galileo gen 2 development board [28] frequently used for commercial purposes
Summary
With exciting development of very large-scale integration (VLSI) technology, Internet of things (IoT) products have rapidly evolved into monitoring devices that can capture and stream multimedia data with high-resolution images. Processing a high volume of image data and wirelessly transmitting data in a multimedia IoT device that typically has limited battery capacity require high-power consumption. This is a major obstacle to system operation time. Many studies have been conducted on energy-effective compression methods based on low memory to reduce power consumption of multimedia IoT systems [6,7,8]. These methods can further reduce power in the overall system and extend the operation time of batteries by lowering processing operations and memory accesses for the compression system
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