Quality Assessment using Tone Mapping Algorithm

Transcription

1 Quaity Assessment using Tone Mapping Agorithm Nandiki.pushpa atha, Kuriti.Rajendra Prasad Research Schoar, Assistant Professor, Vignan s institute of engineering for women, Visakhapatnam, Andhra Pradesh, India ABSTRACT High dynamic range (HDR) images aow us to capture greater uminance eves between its brightest and darkest regions than standard or ow dynamic range (LDR) images. A common probem that is often encountered in practice is concerned about the visuaization of HDR images - most dispay devices avaiabe to us have been designed to accommodate standard LDR images and cannot preserve a information contained in HDR images. In order to visuaize HDR images using standard dispays, a number of tone mapping agorithms have been proposed that convert HDR images to LDR images. Different tone mapping operators (TMOs) create different tone-mapped images and without an appropriate quaity measure different TMOs cannot be compared. In this paper various TMOs are impemented and the quaity of obtained images are assessed using objective parameters such as Peak signa to noise ratio (PSNR), Modified PSNR (MPSNR), Structura fideity, tone mapped quaity inde(tmqi).and it is observed that the parameters obtained by the proposed method are better when compared to the eisting techniques. Keywords: High dynamic range image, image quaity assessment, tone mapping operator. I. INTRODUCTION The rea word scenes ehibit a wide range of uminance (Radiance) variations. Humans, by nature, can gather detai from scenes with etremey varying iuminations and ight patterns. The dynamic range coud be on the order of 0,000 to from highights to shadows. Cameras, on the other hand, do not have such a arge dynamic range. In order to dupicate the vivid detai our eyes can see in a photograph, mutipe eposure images must be taken and combine them to create a singe high dynamic range photograph. HDR imaging is not about showing a the ight in a scene but about gaining contro over a the ight in a scene. A more comprehensive subjective evauation was carried out in [], where tone mapped images generated by fourteen TMOs were shown to two groups of ten human observers to rate LDR images, concerning overa quaity, brightness, contrast, detai reproduction and coor. In [3], subjects were asked to choose the best LDRs derived from two TMOs with different parameter settings to optimay tune the agorithms. The vaue of subjective testing cannot be overestimated. However, they have fundamenta imitations. First, it is epensive and time consuming. Second, it is difficut to be incorporated into an optimization framework to automaticay improve TMOs and adjust their parameter settings. Furthermore, important image structures contained in HDR images may be missing in tone mapped images, but human observers may not be aware of their eistence. In this 05

2 sense, subjective evauation shoud not be regarded images. So, for anaysis of different TMOS prefer the as a goden standard for the quaity of tone mapped objective quaity assessment. II. TONE-MAPPING OPERATORS Tone-mapping operators are divided into two categories. More detais on tone-mapping and a aspects HDR reated may be found in Reinhard et a: High Dynamic Range Imaging - Acquisition, Dispay and Image-Based Lighting. Goba operators These are spatiay uniform, same mapping function is appied to a pies in the image. Loca operators These are spatiay variant, different mapping functions are used based on the oca image content. A number of operators eists that try to mimic the human visua system. The human vision is a fairy compe process with a highy non-inear response. Those tone-mapping operators require the image to be in rea-word units, i.e. cd/m². Many different tone-mapping operators eist, and the optimum operator for a particuar appication and output device might require some eperimentation from the user. Goba operators The simpest possibe tone-mapping is inear mapping, but because most dispay devices ehibit a non-inear response, this wi resut in very dark images if the HDR image has a wide dynamic range. A gamma-corrected inear mapping attempts to correct this probem. Eponentia and ogarithmic corrections to inear mapping are aso possibe. Drago [4] etends the ogarithmic response curves in order to hande a wider dynamic range. A ogarithmic compression is appied to the image uminance. The base of the ogarithm is varied between and 0, based on the brightness of regions within the image. This resuts in a preservation of contrast in darker regions and a higher compression for bright regions. Reinhard et. A [5] took the inspiration to their tonemapping operator from techniques known from traditiona wet-fim photography. To get the visuay best print from a negative, it is not enough to just match contrast and brightness. Scene content, image medium, and viewing conditions must often be considered, too. It was Anse Adams who deveoped Zone System in the 940s. Now, 60 years ater, it is sti used successfuy because it combines quantitative measurements with artistic image content. Loca Operators The Pattanaik [6] muti-scae observer operator attempts to mode a steps within the human visua system currenty known we enough to be modeed. It is the most compete framework to date, athough not stricty speaking necessary for ony reducing the dynamic range. In contrast to the Pattanaik mode, Ashikhmin's [7] operator ony impements those aspects reevant to dynamic range compression. As a resut, the Ashikhmin operator is significanty faster. III. IMAGE QUALITY ASSESSMENT For the anaysis of tone-mapped image quaity assessment there are two methods. The first method is Subjective Quaity Assessment, it provide accurate and reiabe measurements of the quaity of visua signas, but it has some imitations. Second category of quaity assessment methods is Objective Quaity Assessment. The goa of Objective Quaity Assessment is to design mathematica modes that are abe to predict the quaity of an image accuratey and 06

3 aso automaticay. An idea objective Quaity Assessment method shoud be abe to copy the quaity predictions of an average human observer. Based on the avaiabiity of an image quaity assessment, an object quaity assessment is a perfect quaity method. Objective Quaity metrics.peak Signa-to-Noise Ratio It is the ratio between the reference signa and the distortion signa in an image, given in decibes. The higher the PSNR, the coser the distorted image is to the origina. In genera, a higher PSNR vaue shoud correate to a higher quaity image, but tests have shown that this isn't aways the case. However, PSNR is a popuar quaity metric because it's easy and fast to cacuate whie sti giving okay resuts. For images A = {a... am}, B = {b... bm}, and MAX equa to the maimum possibe pie vaue (^8 - = 55 for 8-bit images): R PSNR = 0 og 0 MSE () MPSNR co 55 MSE () = 0og0 Where MSE (A, B) represents mean square error and is given by MMMMMM (AA, BB) = MM (aa MM ii= ii bb ii ) (3). Structura Fideity To measure the structura fideity between reference and distorted images, oca SSIM (structura simiarity inde measurement) was proposed by Wang and Bovik. Using uminance, contrast, and structure terms, the SSIM is defined as mutipication of three terms:. Luminance. Contrast 3. Structure Yeganeh and Wang proposed a modified structura fideity for quaity measure of tone mapped images with a contrast mapping function. The modified SSIM is defined as + C + C SSIM (, y) = y y 3 + y + C y + C3 (4) Where is obtained by a non -inear mapping function defined as 0, < T π T T = + cos ( T ), T T T <, (5) Threshod vaues T and T decide significant contrast range. The modified SSIM does not use uminance term because dynamic range (DR) of and y are much different from each other in computing the quaity measure of tone mapped images. Overa structura fideity score Yeganeh and Wang s method is defined as Q YW = W H i= j= ' SSIM ( i, j) W H Q YW of (6) Where W and H represent width and height of a tone mapped image, respectivey. An HVS (Human visua system) based non-inear mapping function is used to penaize the DR difference between HDR and LDR scenes. 07

4 The DR difference is defined as Naturaness ( τ ) ep θ ' = πθ d (7) Where τ represents a moduation threshod and θ denotes a scae parameter of the norma function. The proposed method uses contrast and structure terms without uminance term, which is epressed as y + C S(, y) = + + C y y + C. + C y 3 3. (8) The proposed method cacuates oca simiarity at pie with m = and quaity score of a tone mapped image is computed as the mean vaue of their oca simiarities. The proposed method uses muti-scae SSIM (MS-SSIM) to verify the performance of the proposed method with the same parameter setting. Quaity map of each scae is defined as Q = W H S i j ms i j i= j= W H ms ( i, j) i= j= (, ) (, ) The quaity score is defined as Q= L Q β = Where L is the tota number of scaes and weight assigned to the -th scae. (9) (0) β is the Tone mapping operators shoud be designed in a way that not ony preserves structura information but aso reproduces natura ooking images. A arge iterature has been dedicated to natura image statistics and their connections to bioogica vision. An eceent review can be found in []. Naturaness has aso been studied in the contet of subjective quaity evauation of tone mapped images. In [], a subjective eperiment was carried out and average correation coefficients between image naturaness and different image attributes such as brightness, contrast, coor reproduction, visibiity and reproduction of detais, are provided. The resuts show that among a attributes being tested, brightness and contrast have more correation with perceived naturaness by subjects. This motivates us to buid our naturaness mode based on these two attributes. To gathered amost bits/pie natura images taken from many different scenes. Figure shows the histograms of the means and standard deviations of these images, which are usefu measures that refect the goba uminance and contrast of images. To find that these histograms can be we fitted using a Gaussian and Beta probabiity density functions, respectivey, where the mode parameters can be found by regression. The fitting curves are aso shown in Fig.. Since brightness and contrast can be considered independent quantities in terms of both natura image statistics and bioogica computation, their joint probabiity density function woud be the product of the two. Therefore, to define our naturaness measure as 08

5 N PP m d K = () Where K is a normaization factor given by K = ma {Pm Pd}. This constrains the statistica naturaness measure to be bounded between 0 and. sensitivities of the two components, respectivey. Since both S and N are upper-bounded by, this overa quaity measure is aso Upper-bounded by. IV. SIMULATION RESULTS Figure : Histograms of (a) means (fitted by Gaussian PDF) and (b) standard deviations (fitted by Beta PDF) of natura images Figure : Generation of LDR image of tower 3. TMQI (Tone-Mapped Quaity Inde) Given a tone mapped LDR image, have two avaiabe measurements, structura fideity S and naturaness N, which are given by above equations respectivey. These two quantities can be used individuay or jointy as a D vector that characterizes different aspects of the quaity of the LDR image. However, in most appications, users woud prefer to have a singe quaity score of the image. Therefore, an overa quaity evauation that combines both quantities is desirabe. In particuar, to define the foowing 3-parameter function to combine the two components ( ) α β Q = as + a N () Figure 3: Generation of LDR image of reading tabe Figure 4: Generation of LDR image of church Where 0 < a < determines the reative importance of the two components, and α and β defines the 09

6 TMO PSNR MPSNR Structura TMQI fideity Reinhard Drago Ashikhmin Tabe : Performance Anaysis of different TMOs for Fig TMO PSNR MPSNR Structura TMQI fideity Reinhard Drago Ashikhmi n Tabe : Performance Anaysis of different TMOs for Fig 3 TMO PSNR MPSNR Structura TMQI fideity Reinhard Drago Ashikhmin Tabe 3: Performance Anaysis of different TMOs CONCLUSION for Fig 4 The overa resuts of our investigation are summarized in tabes. The vaues represent the sum across a subjects and test scenes. Each agorithm was then tested against each other to verify whether they beonged to the same perceptua group or, if indeed one was perceived to be coser to the reference than the other. From the simuation resuts, we can say that goba TMOS Drago and Reinhard are the best TMOS compared to oca TMOS in terms of PSNR, TMQI and aso modified PSNR and Structura fideity. REFERENCES [] M. ˇ Cadík, M. Wimmer, L. Neumann, and A. Artusi, Image attributes and quaity for evauation of tone mapping operators, in Proc. 4 th Pacific Conf. Comput. Graph. App., 006, pp [] M. Barkowsky and P. L. Caet, On the perceptua simiarity of reaistic ooking tone mapped high dynamic range images, in Proc. Int. Conf. Image Process., 00, pp [3] Z. Wang and A. C. Bovik, Modern Image Quaity Assessment.San Rafae, CA: Morgan & Caypoo Pubishers, Mar [4] F. Drago, K. Myszkowski, T. Annen and N. Chiba, Adaptive Logarithmic Mapping For Dispaying High Contrast Scenes in EUROGRAPHICS 003 / P. Brunet and D. Fener, Voume (003), Number 3 [5] Erik Reinhard, Michae Stark, Peter Shirey, James Ferwerda, Photographic Tone Reproduction for Digita Images. ACM Trans. Graph., 3, [6] PATTANAIK, S. N., FERWERDA, J. A., FAIRCHILD, M. D., AND GREENBERG, D. P A mutiscae mode of adaptation and spatia vision for reaistic image dispay. In SIGGRAPH 98: Proceedings of the 5th annua conference on Computer graphics and interactive techniques, ACM Press, New York, NY, USA, [7] ASHIKHMIN, M. 00. A tone mapping agorithm for high contrast images. In EGRW 0: Proceedings of the 3th Eurographics workshop on Rendering, Eurographics Association, Aire-aVie, Switzerand, Switzerand,