Low Light Video Enhancement along with Objective and Subjective Quality Assessment

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...

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Main Authors: Dalasari, Venkata Gopi Krishna, Jayanty, Sri Krishna
Format: Others
Language:English
Published: Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling 2016
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13500
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spelling ndltd-UPSALLA1-oai-DiVA.org-bth-135002016-11-29T05:58:27ZLow Light Video Enhancement along with Objective and Subjective Quality AssessmentengDalasari, Venkata Gopi KrishnaJayanty, Sri KrishnaBlekinge Tekniska Högskola, Institutionen för tillämpad signalbehandlingBlekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling2016Contrast enhancementDynamic rangeKalman filterSpatial denoisingNoise reductionTemporal denoisingTone mapping.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. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:bth-13500application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Contrast enhancement
Dynamic range
Kalman filter
Spatial denoising
Noise reduction
Temporal denoising
Tone mapping.
spellingShingle Contrast enhancement
Dynamic range
Kalman filter
Spatial denoising
Noise reduction
Temporal denoising
Tone mapping.
Dalasari, Venkata Gopi Krishna
Jayanty, Sri Krishna
Low Light Video Enhancement along with Objective and Subjective Quality Assessment
description 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.
author Dalasari, Venkata Gopi Krishna
Jayanty, Sri Krishna
author_facet Dalasari, Venkata Gopi Krishna
Jayanty, Sri Krishna
author_sort Dalasari, Venkata Gopi Krishna
title Low Light Video Enhancement along with Objective and Subjective Quality Assessment
title_short Low Light Video Enhancement along with Objective and Subjective Quality Assessment
title_full Low Light Video Enhancement along with Objective and Subjective Quality Assessment
title_fullStr Low Light Video Enhancement along with Objective and Subjective Quality Assessment
title_full_unstemmed Low Light Video Enhancement along with Objective and Subjective Quality Assessment
title_sort low light video enhancement along with objective and subjective quality assessment
publisher Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling
publishDate 2016
url http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13500
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