An Ingenious Application-Specific Quality Assessment Methods for Compressed Wireless Capsule Endoscopy Images

Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved image quality assessment techniques for wireless capsule endoscopy applications. The proposed objective IQA methods are obtained by modifying the existing full-reference image quality assessment techniques. The modification is done by excluding the noninformative regions, in endoscopic images, in the computation of IQA metrics. The experimental results demonstrate that the proposed IQA method gives an improved peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM). The proposed image quality assessment methods are more reliable for compressed endoscopic capsule images.

[1]  Y. Wang,et al.  Single image super-resolution via adaptive dictionary pair learning for wireless capsule endoscopy image , 2015, 2015 IEEE International Conference on Digital Signal Processing (DSP).

[2]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[3]  P. Swain,et al.  Wireless capsule endoscopy. , 2002, The Israel Medical Association journal : IMAJ.

[4]  John Cosmas,et al.  An Ingenious Design of a High Performance-Low Complexity Image Compressor for Wireless Capsule Endoscopy , 2020, Sensors.

[5]  Mariusz Duplaga,et al.  Hardware-Efficient Low-Power Image Processing System for Wireless Capsule Endoscopy , 2013, IEEE Journal of Biomedical and Health Informatics.

[6]  Lan-Rong Dung,et al.  A Subsample-Based Low-Power Image Compressor for Capsule Gastrointestinal Endoscopy , 2011, EURASIP J. Adv. Signal Process..

[7]  Kinde A. Fante,et al.  A Low-Power Color Mosaic Image Compressor Based on Optimal Combination of 1-D Discrete Wavelet Packet Transform and DPCM for Wireless Capsule Endoscopy , 2015, BIODEVICES.

[8]  Jari Korhonen,et al.  Peak signal-to-noise ratio revisited: Is simple beautiful? , 2012, 2012 Fourth International Workshop on Quality of Multimedia Experience.

[9]  Lan-Rong Dung,et al.  A modified H.264 intra-frame video encoder for capsule endoscope , 2008, 2008 IEEE Biomedical Circuits and Systems Conference.

[10]  P. Karthigaikumar,et al.  Novel chroma subsampling patterns for wireless capsule endoscopy compression , 2019, Neural Computing and Applications.

[11]  Ivar Farup,et al.  Error reduction through post processing for wireless capsule endoscope video , 2020, EURASIP Journal on Image and Video Processing.

[12]  Ljiljana Platisa,et al.  On the Subjective Assessment of the Perceived Quality of Medical Images and Videos , 2018, 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX).

[13]  P. Aparna,et al.  Distributed video coding based on classification of frequency bands with block texture conditioned key frame encoder for wireless capsule endoscopy , 2020, Biomed. Signal Process. Control..

[14]  Lan-Rong Dung,et al.  An ultra-low-power image compressor for capsule endoscope , 2006, Biomedical engineering online.

[15]  Max Q.-H. Meng,et al.  A novel wireless capsule endoscope with JPEG compression engine , 2010, 2010 IEEE International Conference on Automation and Logistics.

[16]  Mohamed El Ansari,et al.  Computer-aided diagnosis system for colon abnormalities detection in wireless capsule endoscopy images , 2017, Multimedia Tools and Applications.

[17]  Daniel Teng,et al.  Efficient hardware implementation of an image compressor for wireless capsule endoscopy applications , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[18]  Kinde A. Fante,et al.  Design and Implementation of Computationally Efficient Image Compressor for Wireless Capsule Endoscopy , 2015, Circuits Syst. Signal Process..

[19]  Fei Gao,et al.  Objective image quality assessment: a survey , 2014, Int. J. Comput. Math..

[20]  Hore,et al.  [IEEE 2010 20th International Conference on Pattern Recognition (ICPR) - Istanbul, Turkey (2010.08.23-2010.08.26)] 2010 20th International Conference on Pattern Recognition - Image Quality Metrics: PSNR vs. SSIM , 2010 .

[21]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[22]  M. G. Martini,et al.  Subjective and objective quality assessment in wireless teleultrasonography imaging , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Muhammad Arslan Usman,et al.  On the suitability of VMAF for quality assessment of medical videos: Medical ultrasound & wireless capsule endoscopy , 2019, Comput. Biol. Medicine.

[24]  Mohammed Ghanbari,et al.  The accuracy of PSNR in predicting video quality for different video scenes and frame rates , 2012, Telecommun. Syst..

[25]  Zoran Kotevski,et al.  Experimental Comparison of PSNR and SSIM Metrics for Video Quality Estimation , 2009, ICT Innovations.

[26]  Pamela C. Cosman,et al.  Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy , 1994, Proc. IEEE.

[27]  Soo Young Shin,et al.  Quality assessment for wireless capsule endoscopy videos compressed via HEVC: From diagnostic quality to visual perception , 2017, Comput. Biol. Medicine.

[28]  Mariusz Duplaga,et al.  Low power FPGA-based image processing core for wireless capsule endoscopy , 2011 .

[29]  Zhou Wang,et al.  Modern Image Quality Assessment , 2006, Modern Image Quality Assessment.

[30]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.