Abstract
The energy autonomy and the lifetime of battery-operated sensors are primary concerns in industrial, healthcare and IoT applications, in particular when a high amount of data needs to be sent wirelessly such as in Wireless Camera Sensors (WCS). Onboard real-time image compression is the appropriate solution to decrease the system’s energy. This paper proposes an optimized algorithm implementation tailored for PULP (Parallel Ultra Low Power) processors, that permits to shrink the image size and the data to transmit. Our optimized JPEG encoder based on a Fast-Discrete Cosine Transform (DCT) function is designed to achieve the best trade-off between energy consumption and image distortion. The parallel software implementation requires only 0.495 mJ per frame and can support up to 80 fps satisfying the most stringent requirements in WCSs applications without requiring a dedicated hardware accelerator.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Magno M et al (2013 June) Multimodal video analysis on self-powered resource-limited wireless smart camera. IEEE J Emerg Sel Top Circuits Syst 3(2):223–235
Magno M et al (2009 Sept) Multimodal abandoned/removed object detection for low power video surveillance systems. In: 2009 Sixth IEEE international conference on advanced video and signal based surveillance, Genova, pp 188–193
Polonelli T et al (2019 June) A multi-protocol system for configurable data streaming on IoT healthcare devices. In: 2019 IEEE 8th international workshop on advances in sensors and interfaces (IWASI), Otranto, Italy, pp 112–117
Negri L et al (2004 Aug) FSM-based power modeling of wireless protocols: the case of Bluetooth. In Proceedings of the 2004 international symposium on low power electronics and design (IEEE Cat. No.04TH8758), Newport Beach, CA, USA, pp 369–374
Ballerini M et al (2019 July) Experimental evaluation on NB-IoT and LoRaWAN for industrial and IoT applications. In: 2019 IEEE 19th international conference on industrial informatics (INDIN), Helsinki, 2019
Makkaoui L et al (2010 July) Fast zonal DCT-based image compression for wireless camera sensor networks. In: 2010 2nd international conference on image processing theory, tools and applications. IEEE, pp 126–129
Rusci M et al (2016) An event-driven ultra-low-power smart visual sensor. IEEE Sens J 16(13):5344–5353
Chen S et al (2011) A 64 × 64 Pixels UWB wireless temporal-difference digital image sensor. IEEE Trans Very Large Scale Integr (VLSI) Syst 20(12):2232–2240
Torfs T et al (2012) Low power wireless sensor network for building monitoring. IEEE Sens J 13(3):909–915
Rossi D et al (2015 Oct) A −1.8 V to 0.9 V body bias, 60 GOPS/W 4-core cluster in low-power 28 nm UTBB FD-SOI technology. In: 2015 IEEE SOI-3D-subthreshold microelectronics technology unified conference (S3S). IEEE, pp 1–3
Osman H et al (2007 Nov) JPEG encoder for low-cost FPGAs. In: 2007 international conference on computer engineering & systems. IEEE, pp 406–411
Sakamoto T et al (1998) Software JPEG for a 32-bit MCU with dual issue. IEEE Trans Consum Electron 44(4):1334–1341
Rao K et al (2014). Discrete cosine transform: algorithms, advantages, applications. Academic Press
Chen W et al (1977) A fast computational algorithm for the discrete cosine transform. IEEE Trans Commun 25(9):1004–1009
Hou H (1987) A fast recursive algorithm for computing the discrete cosine transform. IEEE Trans Acoust Speech Signal Process 35(10):1455–1461
Loeffler C et al (1989 May) Practical fast 1-D DCT algorithms with 11 multiplications. In: International conference on acoustics, speech, and signal processing. IEEE, pp 988–991
Arai Y et al (1988) A fast DCT-SQ scheme for images. IEICE Trans (1976–1990) 71(11):1095–1097
Noritsuna 2019, https://github.com/noritsuna/JPEGEncoder4Cortex-M. Available online: July 2019
Moodstocks 2016, https://github.com/Moodstocks/jpec. Available online: July 2019
Flamand E et al (2018 July) GAP-8: a RISC-V SoC for AI at the edge of the IoT. In: 2018 IEEE 29th international conference on application-specific systems, architectures and processors (ASAP). IEEE, pp 1–4
Hore A et al (2010 Aug) Image quality metrics: PSNR vs. SSIM. In: 2010 20th international conference on pattern recognition. IEEE, pp 2366–2369
Polonelli T et al (2018 Oct) Slotted ALOHA overlay on LoRaWAN-A distributed synchronization approach. In: 2018 IEEE 16th international conference on embedded and ubiquitous computing (EUC). IEEE, pp 129–132
Polonelli T et al (2019 Feb) Slotted ALOHA on LoRaWAN-design, analysis, and deployment. In: Sensors (Switzerland), 19(4)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Polonelli, T., Battistini, D., Rusci, M., Brunelli, D., Benini, L. (2020). An Energy Optimized JPEG Encoder for Parallel Ultra-Low-Power Processing-Platforms. In: Saponara, S., De Gloria, A. (eds) Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2019. Lecture Notes in Electrical Engineering, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-37277-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-37277-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37276-7
Online ISBN: 978-3-030-37277-4
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)