Impact Of Fixed Pattern Noise On Embedded Image Compression Techniques

• doi: 10.1109/ISCAS.2017.8050547
• title: Impact of fixed pattern noise on embedded image compression techniques
• publisher: IEEE
• isbn: 978-1-5090-1427-9
• issn: 2379-447X
• rank: 2724
• access_type: LOCKED
• content_type: Conferences
• abstract: This paper discusses the impact of Fixed Pattern Noise (FPN) on image compression embedded in CMOS sensors. This FPN is mainly due to technology dispersions as well as a non uniform topology of the layout. FPN is generally modelled as a non uniform affine mapping of the pixels. The Dark Signal Non-Uniformity (DSNU) represents the level of offset variations while Photo Response Non-Uniformity (PRNU) is the level of pixel gain variations. Once those two parameters are properly calibrated for each pixel, the image can be corrected by linear interpolations (i.e. affine correction). This operation requires specific processing and large memory resources to be implemented directly in the focal plane. If this 2-points correction is not performed inside the sensor, some problems arise in the context of embedded compression because the hypothesis on image sparsity is no longer satisfied. By considering this context, this paper presents a comparative study between a 2D Haar wavelet compression, separable Compressive Sensing (CS) and a novel approach combining wavelet based compression and row based CS. In the case of a high non uniformity of the focal plane (e.g. considerable technological dispersion), it appears that a proper combination of CS and Haar takes advantage of both enabling FPN correction at the reconstruction stage and outperforming the alternative techniques.
• article_number: 8050547
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8050547
• html_url: https://ieeexplore.ieee.org/document/8050547/
• abstract_url: https://ieeexplore.ieee.org/document/8050547/
• publication_title: 2017 IEEE International Symposium on Circuits and Systems (ISCAS)
• conference_location: Baltimore, MD, USA
• conference_dates: 28-31 May 2017
• publication_number: 8014728
• is_number: 8049747
• publication_year: 2017
• publication_date: 28-31 May 2017
• start_page: 1
• end_page: 4
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 369
• insert_date: 20170928
• index_terms:
  • ieee_terms:
    • Image coding
    • Image reconstruction
    • Sensors
    • Bit rate
    • Entropy
    • CMOS image sensors
    • Transforms
  • author_terms:
    • Wavelet image compression
    • Compressive sensing
    • advanced CMOS image sensor
    • Fixed Pattern Noise
  • dynamic_index_terms:
    • Source Code
    • Data Compression
    • Image Compression
    • Fixed Pattern Noise
    • Linear Interpolation
    • Focal Plane
    • Back Focal Plane
    • Image Sensor
    • Camera Sensor
    • Haar Wavelet
    • Sparse Imaging
    • Dark Signal
    • Reconstruction Stage
    • Analog-to-digital Converter
    • Analog-to-digital
    • Digital-to-analog Converter
    • Reconstruction Algorithm
    • Bitrate
    • Mixed Strategy
    • Strategy Profile
    • Signal Representation
    • Measurement Matrix
    • Measurement Matrices
    • Wavelet Decomposition
    • Discrete Cosine Transform
    • Regularization Problem
    • Entropy Coding
    • Entropy Encoding
    • Unwanted Artifacts
    • Unwanted Artefacts
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 978-1-5090-1427-9, isbnType: New-2005
  • format: Electronic ISBN, value: 978-1-4673-6853-7, isbnType: New-2005
• authors:
  • Author Name: William Guicquero
    Affiliation: Univ. Grenoble Alpes, Grenoble
    Author URL: https://ieeexplore.ieee.org/author/37085381310
    ID: 37085381310
    Order: 1
    Author Affiliations:
    • Univ. Grenoble Alpes, Grenoble
    • FR CEA-LETI, Grenoble, FR
  • Author Name: Laurent Alacoque
    Affiliation: Univ. Grenoble Alpes, Grenoble
    Author URL: https://ieeexplore.ieee.org/author/38072935800
    ID: 38072935800
    Order: 2
    Author Affiliations:
    • Univ. Grenoble Alpes, Grenoble
    • FR CEA-LETI, Grenoble, FR

Image Sensor

• sensor_type: CMOS
• resolution: 512x512
• dynamic_range: N/A
• pixel_size: N/A
• dark_current: N/A

Optical Data

• focal_length: N/A
• aperture: N/A
• field_of_view: N/A
• distortion: N/A

Performance Metrics

• frame_rate: N/A
• signal_to_noise_ratio: N/A
• sensitivity: N/A
• shutter_speed: N/A
• power_consumption: N/A
• noise: N/A

Applications & Benefits

• cell_imaging: N/A
• benefits: Data compression techniques discussed for reducing power consumption in embedded image processing.

Supporting Organizations

• supported_by: Univ. Grenoble Alpes, CEA-LETI

Manuscript Details

• publication_date: 28-31 May 2017

Relevancy Score

• score: 8
• missing_fields:
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • power_consumption
  • noise

Processed JSON Filename

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