Image Quality Assessment of Enriched Tonal Levels Images

The quality assessment of a high dynamic image is a challenging task. The few available no reference image quality methods for high dynamic range images are generally in evaluation stage. The most available image quality assessment methods are designed to assess low dynamic range images. In the paper, we show the assessment of high dynamic range images which are generated by utilizing a virtually flexible fill factor on the sensor images. We present a new method in the assessment process and evaluate the amount of improvement of the generated high dynamic images in comparison to original ones. The results show that the generated images not only have more number of tonal levels in comparison to original ones but also the dynamic range of images have significantly increased due to the measurable improvement values.

[1]  Phong V. Vu,et al.  A Fast Wavelet-Based Algorithm for Global and Local Image Sharpness Estimation , 2012, IEEE Signal Processing Letters.

[2]  Mikko Nuutinen,et al.  CID2013: A Database for Evaluating No-Reference Image Quality Assessment Algorithms , 2015, IEEE Transactions on Image Processing.

[3]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[4]  Alan C. Bovik,et al.  No-reference image quality assessment for high dynamic range images , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

[5]  Rafal Mantiuk,et al.  A comparative review of tone‐mapping algorithms for high dynamic range video , 2017, Comput. Graph. Forum.

[6]  D. B. Judd.  Hue Saturation and Lightness of Surface Colors with Chromatic Illumination , 1940 .

[7]  Alan C. Bovik,et al.  A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.

[8]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH '08.

[9]  Hans-Peter Seidel,et al.  Lightness Perception in Tone Reproduction for High Dynamic Range Images , 2005, Comput. Graph. Forum.

[10]  Silvano Donati,et al.  Microconcentrators to recover fill-factor in image photodetectors with pixel on-board processing circuits. , 2007, Optics express.

[11]  Wei Wen,et al.  Novel Software-Based Method to Widen Dynamic Range of CCD Sensor Images , 2015, ICIG.

[12]  Bernd Hoefflinger.  High-dynamic-range (HDR) vision : microelectronics, image processing, computer graphics , 2007 .

[13]  M. Deguchi,et al.  Microlens design using simulation program for CCD image sensor , 1992 .

[14]  Phong V. Vu.  On the use of image sharpness to JPEG2000 no-reference image quality assessment , 2013 .

[15]  Bernd Hoefflinger,et al.  High-Dynamic-Range (HDR) Vision , 2007 .

[16]  Wei Wen,et al.  BACK TO BASICS: TOWARDS NOVEL COMPUTATION AND ARRANGEMENT OF SPATIAL SENSORY IN IMAGES , 2016 .

[17]  Werner Purgathofer,et al.  Tone Reproduction and Physically Based Spectral Rendering , 2002, Eurographics.

[18]  Thomas Bashford-Rogers,et al.  Dynamic range compression by differential zone mapping based on psychophysical experiments , 2012, SAP.

[19]  Arun N. Netravali,et al.  Digital Pictures: Representation, Compression and Standards , 1995 .

[20]  D. L. Macadam.  Projective Transformations of I. C. I. Color Specifications , 1937 .

[21]  Zhou Wang,et al.  Objective Quality Assessment of Tone-Mapped Images , 2013, IEEE Transactions on Image Processing.

[22]  E. Rossi,et al.  The relationship between visual resolution and cone spacing in the human fovea , 2009, Nature Neuroscience.

[23]  Dani Lischinski,et al.  Gradient Domain High Dynamic Range Compression , 2023 .