Change search
ReferencesLink to record
Permanent link

Direct link
Low Light Video Enhancement along with Objective and Subjective Quality Assessment
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2016 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Enhancing low light videos has been quite a challenge over the years. A video taken in low light always has the issues of low dynamic range and high noise. This master thesis presents contribution within the field of low light video enhancement. Three models are proposed with different tone mapping algorithms for extremely low light low quality video enhancement. For temporal noise removal, a motion compensated kalman structure is presented. Dynamic range of the low light video is stretched using three different methods. In Model 1, dynamic range is increased by adjustment of RGB histograms using gamma correction with a modified version of adaptive clipping thresholds. In Model 2, a shape preserving dynamic range stretch of the RGB histogram is applied using SMQT. In Model 3, contrast enhancement is done using CLAHE. In the final stage, the residual noise is removed using an efficient NLM. The performance of the models are compared on various Objective VQA metrics like NIQE, GCF and SSIM.

To evaluate the actual performance of the models subjective tests are conducted, due to the large number of applications that target humans as the end user of the video.The performance of the three models are compared for a total of ten real time input videos taken in extremely low light environment. A total of 25 human observers subjectively evaluated the performance of the three models based on the parameters: contrast, visibility, visually pleasing, amount of noise and overall quality. A detailed statistical evaluation of the relative performance of the three models is also provided.

Place, publisher, year, edition, pages
2016. , 72 p.
Keyword [en]
Contrast enhancement, Dynamic range, Kalman filter, Spatial denoising, Noise reduction, Temporal denoising, Tone mapping.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-13500OAI: oai:DiVA.org:bth-13500DiVA: diva2:1049221
External cooperation
Sällberg Technologies e.U.
Subject / course
ET2566 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal processing
Educational program
ETASX Master of Science Programme in Electrical Engineering with emphasis on Signal Processing
Supervisors
Examiners
Available from: 2016-11-28 Created: 2016-11-23 Last updated: 2016-11-28Bibliographically approved

Open Access in DiVA

fulltext(10974 kB)7 downloads
File information
File name FULLTEXT02.pdfFile size 10974 kBChecksum SHA-512
ee0e793cabfc0ce16c58a670dfe4db5b0318b4bc35ac4fc43aa6d29a87f250ffd0ad6b2653cbbdd514352a4e15438cd8b5140281c812ed27543670a094cb7f30
Type fulltextMimetype application/pdf

By organisation
Department of Applied Signal Processing
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 7 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 38 hits
ReferencesLink to record
Permanent link

Direct link