eprintid: 1510
rev_number: 14
eprint_status: archive
userid: 35
dir: disk0/00/00/15/10
datestamp: 2013-03-06 08:36:51
lastmod: 2013-09-17 10:35:52
status_changed: 2013-03-06 08:36:51
type: conference_item
metadata_visibility: show
creators_name: Minervini, Massimo
creators_name: Tsaftaris, Sotirios A.
creators_id: massimo.minervini@imtlucca.it
creators_id: sotirios.tsaftaris@imtlucca.it
title: Application-Aware Image Compression for Low Cost and Distributed Plant Phenotyping
ispublished: pub
subjects: QA75
subjects: TD
divisions: CSA
full_text_status: none
pres_type: paper
abstract: Plant phenotyping investigates how a plant's genome, interacting with the environment, affects the observable traits of a plant (phenome). It is becoming increasingly important in our quest towards efficient and sustainable agriculture. While sequencing the genome is becoming increasingly efficient, acquiring phenotype information has remained largely of low throughput, since high throughput solutions are costly and not widespread. A distributed approach could provide a low cost solution, offering high accuracy and throughput. A sensor of low computational power acquires time-lapse images of plants and sends them to an analysis system with higher computational and storage capacity (e.g., a service running on a cloud infrastructure). However, such system requires the transmission of imaging data from sensor to receiver, which necessitates their lossy compression to reduce bandwidth requirements. In this paper, we propose an application aware image compression approach where the sensor is aware of its context (i.e., imaging plants) and takes advantage of the feedback from the receiver to focus bitrate on regions of interest (ROI). We use JPEG 2000 with ROI coding, and thus remain standard compliant, and offer a solution that is low cost and has low computational requirements. We evaluate our solution in several images of Arabidopsis thaliana phenotyping experiments, and we show that both for traditional metrics (such as PSNR) and application aware metrics, the performance of the proposed solution provides a 70% reduction of bitrate for equivalent performance.
date: 2013-07
date_type: submitted
event_title: 18th International Conference On Digital Signal Processing
event_location: Santorini, Greece
event_dates: 1-3 July 2013
event_type: conference
refereed: FALSE
related_url_url: http://dsp2013.dspconferences.org/index.php?option=com_content&view=frontpage&Itemid=2
related_url_type: org
citation:   Minervini, Massimo and Tsaftaris, Sotirios A.  Application-Aware Image Compression for Low Cost and Distributed Plant Phenotyping.  In: 18th International Conference On Digital Signal Processing, 1-3 July 2013, Santorini, Greece         (2013)