Abstract
Color Vision Deficiency (CVD) has a widespread impact and has affected more than 300 million people across the world. CVD has three major types as Monochromacy, Dichromacy and Anomalous Trichromacy. We design and develop a website with re-coloring algorithms incorporated for anomalous trichromats and dichromats. Unlike an image recoloring schemes, recoloring a video demands maintaining color consistency in the frames which implies similar color in adjacent frames should be recolored to similar necessary color. Our aim is to show the Color Vision Deficient (CVD) the primary colors distinguishably, which they cannot see in normal lighting conditions. To achieve this goal, we have incorporated recoloring algorithm into the website using Matlab as a back-end language, which will first conduct Ishihara plates test to check if a user is CVD. If yes, the test will then comment on the type of its Color Blindness, based on this user would be recommended to go to the respective module of recoloring. User would be asked to submit video or an image which is to be re-colored. Video/Image would be re-colored on server side, the recolored output would be displayed on a screen with an option to download result. The RGB to LMS algorithm is used to recolor those pixels in images and videos which are unperceivable to users. After recoloring they will be able to distinguish the colors and it will be more recognizable. Finally, there would be a feedback/suggestions which users are asked to fill-out, to understand the performance of algorithm and quality of re-colored image. Experimental results prove that with the help of our website almost all CVD irrespective of their types would be helped in their vision to experience multimedia. Thus our website would aid millions of CVD’s to experience colors in multimedia which they are unable to see distinguishably.
Keywords
Introduction
Color-blind deficiency, is found more in men as compared to women. Statistically it’s approximately 5% of the population (i.e. 8% of males and 0.5% of females) or five out of every hundred users who are exposed to computer usage in their daily life. The prominent CVD is a red-green sensitivity or called Protanomaly according to Huang et al. [1]. People with CVD find it difficult to distinguish the certain color combinations impacting their ability to perform tasks related to color and visualization. While a lot of research and pragmatic experimentation has been done in medical and genetics, a perennial solution yet to be found. In fact, all the results have been ephemeral. Therefore, research in the area of image processing to help color blind people distinguish and perceive objects has engendered. This deficiency has been fostered by deformity of rod and cone cells that are situated within the retina as stated by Carlson et al. [3]. Cone cells help to distinguish the color and rod cell regulates the light intensity. Cone cells are of three types they are the red, blue, green cone cell. Color blind deficiency is the disability that can hardly be altered or cured. In most cases, it is hereditary or genetic and the condition stays the same throughout. It doesn’t get better or worse. As technology bloomed and all of a sudden massive demand for multimedia became a necessity in one’s life but CVD miss out on important details in images and videos due to the deficiency in their retina.
They have difficulties in viewing traffic lights, digital images and videos. It happens because of genes which are inherited through the color blind parents. Genes are responsible for it. Discrimination against the color blind deficient individuals is evident at workplaces, even if they carry out the work properly.
Color blind deficiency individuals are more in numbers and their difficulties just cannot be ignored. Each person’s severity of impairment may differ depending on the extent of the shift in the wavelength of color distinction. More than 20 nm shift in spectral response of a cone is essentially equivalent to complete blindness that is received by that specific cone cell. Retina produces the impulse that will be received by the human brain in the occipital lobe and then it will be represented as a color. Carlson et al. [3] believed that a universal condition of the eye can be given by Ishihara test. In our website we have incorporated Ishihara test, which will give the color blindness type as well. The Ishihara test is one of the methods to determine whether the person has CVD or not. However, it’s still unable to categorize a color deficient individual with accurate detail and perfection since its inception in 1917. The Ishihara tests obtainable today justifies whether people have CVD. For any CVD individual to see the recolored image, it is first important to check which primary colors are not easily detected by him/her under normal lighting conditions. Hence the following are various types of color vision deficiencies:
Monochromacy: This vision have either no cones or one cone present in the retina and can see no color. Their vision consists of different shades of grey color. Dichromacy: Two different cone types are present, the one is missing completely
Protanopia: CVD cannot see Red color completely (Missing L-cone) Deuteranopia: CVD cannot see Green color completely (Missing M-cone) Tritanopia CVD cannot see Blue color completely (Missing S-cone).
Anomalous trichromacy:
Protanomaly: CVD cannot distinguish between Red-Green (Malfunctioning in L/M cone) Deuteranomaly: CVD cannot distinguish between Green-Blue (Malfunctioning in M/S cone) Tritanomaly: CVD cannot distinguish between Red-Blue (Malfunctioning in L/S cone).
Thus due to an increasing number of CVD individuals daily and no cure being developed so far for color blindness, it is need of humanity to develop some sort of solution which can help the CVD’s to perceive multimedia of all the three primary colors distinguishably even under normal lighting conditions.
The remainder of this paper discusses about the layout of the research work in sections. Section 2 discusses about the background. Section 3 discusses about the proposed methodology, Material and Method are discussed in Section 4, and Section 5 is about numerical analysis. Section 6 is about experimental results and Section 7 highlights about the conclusion of our research work.
A number of studies have been reported in literature which is focused on colors which plays a vital part in all sorts of activities. Images are often analyzed by its meta-data like size, type, form etc. Identifying the change in the intensity between two different regions in an image can be done by the edges, which leads to get more details about the object, its size and shape. Dody et al. [4] have discussed about few applications of edge detection including surveillance, text and shape detection. The human eye accommodates two forms of image receptors: namely rods and cones clearly stated by Navada et al. [5]. The rod cells are responsive to dim light levels which do not distinguish between colors. Brighter light levels are responsive because of cones, which allow us to see different colors. Existence of three types of cones make color perception possible: long, medium and short wavelength cones. They each correlate to a specific light wavelengths that epitomize the three basic colors that are red (long wavelength), green (medium) and blue (short) as shown in Fig. 1. Vision that utilizes three receptors for color perception is called as normal vision.
Wavelength of the cone system depicting normal vision [5].
Color Vision Deficiency can be identified through an objective based eye examination called as Ishihara plates. Liu et al. [12] indicated, the patient sees a series of specially designed images (plates) composed of different close frequency color dots, called pseudo isochromatic plates. Pseudo isochromatic plate testing determines if a color vision deficiency exists and also the form of deficiency.
CVD’s find difficulty in identifying the different colors in traffic signals, fruits, vegetables [11]. They used certain functions to analyze and recolor the entire websites for anomalous trichromats; it also ensures that it maintains the ratio between the color differences; perceived by trichromats and anomalous trichromats. It is based on the genetic algorithm [8].
As far as we know, there are only a handful of works, some of them by Huang et al. [17], Jeong et al. [18], regarding re-coloring of videos for color deficient individuals. In [21], Machado et al. showcase a re-coloring algorithm for enhancing color contrasts for dichromats using GPU. In [1], Huang and Chiu focused on TCC based video reproduction for dichromats specifically. However, they did not guarantee the similar color appearance in different frames to be remapped to the new color uniformly as the temporal relationship between frames was not taken into accountability.
Several research works have been done out to mitigate the problem of unperceivable colors for CVD so they can perceive visual objects with different combinations of colors and color differences. A mapping function can be adapted to change the unperceivable colors by CVD to colors perceived with different colors so they can distinguish it. Recoloring algorithms have two problems first they have huge computational cost as most of the recoloring algorithms use an optimization process to determine the color mapping function and the other flaw is maintaining of naturalness. It arises because of the inconsistency of colors while recoloring of different frames of videos. The various systems that already exist are the Color Correction System invented by A. Thomas who designed contact lenses and glasses for corrective vision. The wavelength of each color that goes into the eyes is changed by the filters, effectively making one to see more colors. The ColorAdd System developed by Miguel Neiva allows CVD for navigation, reference and organization. The EyePlot is operable on Macintosh and Windows Operating Systems.
We are focusing on all types of color blindness except for Monochromacy as they perceive only grey levels, recoloring or wavelength adjustment will not significantly help the CVD’s as they will again only experience grey levels of recolored multimedia.
We conducted the clinical analysis on 300 participants under the guidance of Municipal Corporation of Greater Mumbai’s Department of Ophthalmology, Topiwala National Medical College and B.Y.L. Nair Hospital. The Ethical Review committee performed the ethical review of research and gave a protocol number. “Enabling feature distinction and preserving naturalness in visual media using the combined video processing approach for color deficient individuals Version 1 dated 27
Types of color blindness and their amount of rareness
As discussed in abstract, our recoloring algorithm is incorporated into the website whose functionality is written in Matlab programming language which will check whether the user is CVD or not. If yes then the user is asked to submit the video or image. The advantage of this website is that the recolored image could be processed within seconds and the output would be displayed on a screen with the download option. The limitation of website is that the video is completely broken into frames and each frame is then recolored as per the respective type of color-vision deficiency and then the recolored frames would be then clustered back to form a recolored video, so this complete process requires some decent amount of time. Hence, for processing of videos the user is asked to submit video and check after sometime approximately to get back the recolored video in downloadable format. Timings are discussed in the result section. The following is the block diagram for video/image recoloring.
Block diagram of recoloring process of our system for both images and videos.
Flow chart for recoloration of a video.
Now to understand each and every algorithm as per type of color vision deficiency, we must have to take a look at the following table, to understand types of color vision deficiencies and the amount of rareness.
The proposed methodology consists of a method to modify an image/video in order for it to be more perceivable for those suffering from (Dichromacy and Anomalous Trichromacy).
The proposed methodology consists of the algorithm for Images:
Accept the input image and store it in browser cache Extract RGB co-ordinates Multiply with respective co-ordinates Generate output of each type of deficiency; for the given input image Display the image’s output The proposed methodology consists of the algorithm for Video Recoloring:
Accept the input video, and store it in a browser cache. Extract Audio. Break video into shots. Convert shot to frames. Extraction of frames and loading. Differentiation of CBP and CBU colors. Re-mapping of the color. Recombining of the frames to shots. We have used File System to store original and processed multimedia files like images and videos to demonstrate an image and its output. An original frame is processed and recolored three times with names as the simulated image, the recolored image and the simulated recolored image. The simulated image means what the color vision deficient perceives from the original image before recoloring it. The recolored image means what the color vision deficient is expected to see and perceive all 3 primary colors. The simulated recolored image is the image, color vision deficient will perceive from the recolored image. The file system clearly defines the input and output of multimedia files. We are using Matlab libraries to process the files. All these images are used to populate the website data for recoloring the multimedia. In nutshell, we are not using any traditional database like MySQL or Oracle.
This research is carried out by understanding the difficulties faced by CVD individuals. An enormous amount of information is passed through television, cell phones and the internet. This makes the necessity of modifying video and image for CVD users. Therefore, using image processing, the digital image can be enhanced and modified to get more sharpening, removal of noise, rotation, scaling. We apply RGB to LMS re-coloring algorithm based on the threshold value of the pixel to change the color of those pixels that are unperceivable to color deficient individuals.
Steps of video recoloring
Detection of shots
Each scene is made of several shots. A shot is the continuous footage or sequence between two edits or cuts of a camera roll. Frames are nothing but still images achieved by the shots as in [1], shot detection is performed and each frame of the shot is processed as a single image, after completion of all frames of a shot, the frames of the next shot are processed.
Extraction of frames and loading
A shot consists of several frames which are been detected. Each frame is loaded one at a time and processed individually and re-coloring algorithm is applied on each of them. A color appearing in multiple frames should be remapped to the same color in all frames should be done by the recoloring algorithm.
Differentiating CBP and CBU colors
Colors that are not perceivable to color deficient individuals have to be remapped from that particular frame. We need to distinguish the color blind perceivable colors from the color blind unperceivable colors [6]. A simulated image is created for distinguishing purpose, this is the image how it is seen by the colorblind.
The following procedure is followed:
Pre-processing
Trichromatic vision is if all three cones
Color plane in LMS Space is defined as:
Where
As per Eq. (1).
For example, in deuteranopia the
The following steps are applied to each image pixel represented by RGB color coordinates:
Gamma correction: Gamma is the relationship between RGB intensity values of an image. The relationship between input and output is that output is proportional to the input raised to the power of gamma.
Using LMS Color Space for recolorization RGB corresponds to the phosphor levels on a cathode ray tube screen to match the colors used to define digital images. Transformation of RGB to XYZ to LMS:
Transformation of 3D LMS space to dichromats 2D spaces solving the plane equation for deuteranopes:
Inverse transform LiMiSi to XYZ to RGB,
Inverse gamma correction:
Similarly conversion matrix for protanopes is:
Similarly conversion matrix for tritanopes is
The complete algorithm of tritanopia is explained in the Section 6.3.
In step [4], detection of the unperceivable colors is done by first applying the red mask on the pixels. As the colors get separated into subsets of perceivable and unperceivable colors. The
From the above steps we know which pixels are unperceivable we have to remap them [1]. For this we use two strategies as explained in step no 4.
Which strategy is to be used depends on the temporal color difference (
Global rotation of color component X.
4.1.2.1 Spatial Color Mapping Strategy (CMS
)
In CMS’s each CBU color X is recolored to X’ with the angle
Where
The global rotation angle is for discrimination of the color blind perceivable colors from the color blind unperceivable colors. Hence, the global rotation angle is directly proportional to the difference between the original pixel value and value for a simulated pixel.
The local contrast among CBU colors needs to be enhanced so the local rotation angle (
4.1.2.2 The Temporal Color Mapping Strategy (CMS
)
This method is adopted when there is not large distance between the original pixel color and simulated pixel color. It is done to reduce computational cost and at the same time maintain temporal color consistency. In this method, we apply color prediction process to find the pixel that has the most similar color in the previous frame. Then linear color prediction (LCP) is used to find the remapped color.
The process of LCP is as follows, let
Then remapped color X’ is defined as:
To increase computational efficiency even further, we may take a second threshold such that if the linear difference is below this threshold value then directly map
Finally, after all the frames of a shot are transformed, these frames are recombined to form the particular shot. This shot will then be played. In case of image recoloring, the image is first loaded, then CBU and CBP colors are distinguished just as they are done in video recoloring. Next remapping of CBU is done using spatial color mapping strategy. The image produced after this stage is the transformed image.
Numerical analysis
Once the shot detection is done the ith shot As the shots are achieved from the video then extraction of colors from each shot needs to be done. Shot volume in reality remains is too large so assembling all the pixel values for clustering shot key colors needs to be also considered. Elimination of similar pixel values between frames which are adjacent can be done in advance. For pruning of the repetitive colors the motion estimation approach [22] is applied. As frames in the same shot are quite similar most image blocks in the tth frame The number of colors to be quantized in
The total no
We compute the inter color change rate of frames, color reduction score and contrast reduction score. Contrast reduction score is the ratio of the mean contrasts of the original and remapping versions to describe quantitatively the difference in contrast. Perceptual distances are considered between the pairs of a point and its neighbor point at one pixel apart to compute color contrast. If the color contrast after remapping are close the original one, it implies the remapping method can maintain the color contrast. The distinguishable colors mean the colors perceived by viewers. The color reduction score is the ratio of the mean number of distinguishable colors of the original and the remapping versions of videos. The ICCR score is the inter color change rate of frame is used to evaluate the color changes between the frames. Let
where The mean of colors contrast is the average of the color contrast of all frames of each video. While the contrast reduction score is closer to one, the reproduced video is more similar to the original video. In the implementation of the proposed algorithm, the color accessibility in the image-level is evaluated. The Naturalness indicates how much distortion occurs through the recoloring process is calculated by,
Where
Protanopia and protanomaly
As these types of CVDs cannot see the red color completely or confuse between the red and the green color. It is very difficult for them to do daily activities which involves the red color in it. The best example being traffic signal or any danger signs; which are usually in the red color. The following algorithm proposed for the Protanopic CVD which will help in the way that CVD will be able to get a basic difference between the red shades it perceives and after recoloring will experience those shades in a better way to understand the overall content of the image. The efficiency of the algorithm is increased by considering the following factors:
Increasing the difference between the color blind unperceptive and color blind perceptive colors, improves the color perceptibility of the image. A real-time operation is necessary. Temporal color consistency be preserved. Enhancement of local contrast in the region of the color blind unperceptive color needs to be done.
Algorithm which is designed is much more efficient than the existing techniques of re-coloring with the advantage of recoloring a video of both types be it 30 FPS (Frames per second) or 60 FPS. The Algorithm is further compared on the basis of experimental results with other existing techniques of recoloring in the table based on processing time for generating output recolored the image as the primary parameter. The following is the block diagram for algorithm.
(a) An original brain image, (b) Simulated image (Original image as seen by protanopia), (c) Recolored image, (d) Simulated recolored image as seen by protanopia.
(a) An original gaugin image, (b) The simulated image, (c) The recolored image, (d) The simulated recolored.
Carom video results (a) An original image, (b) The simulated image sequence for protanopia, (c) The recoloring method, (d) The simulated recolored frames.
The results of above algorithm for Protanopia in image and video are explained in the detail manner with respect to images and videos with various important coefficients such as Mean, Naturalness and Naturalness assessment as well as quality factors are also given such as Contrast Reduction Score, Color Reduction score and ICCR are explained in detail.
The figure given above displays the result of the proposed system for brain image (Fig. 5a). As protanopes are unable to differentiate between red and black, they cannot distinguish between regions in the image (Fig. 5b). To overcome this drawback, we recolored the image (Fig. 5c). After re-coloring the person suffering from protanopia will be able to differentiate between the red and black region (Fig. 5d).
The figure given above displays, the result of the proposed system for a Gaugin image (Fig. 6a). As protanopes are unable to differentiate between brownish red and green in the given plane, they cannot distinguish between regions in the given plane (Fig. 6b). To overcome this drawback, we re-colored the image (Fig. 6c). After re-coloring person suffering from protanopia will be able to differentiate between the regions of the plane (Fig. 5d).
Some important coefficients for recolored image
Some important coefficients for recolored image
Quality factors and co-efficient for the recolored image
As shown in the above Fig. 7b, 17
Coefficients for quality of recolored video
Coefficients for quality of recolored video
Complex coefficients calculation for performance assessment
Complex co-efficient for performance parameters
The results for Protanomaly are given in the image as well in video are as follows.
(a) An original image, (b) The simulated image, (c) The simulated recolored image, (d) The recolored image.
(a) An original image, (b) The simulated image, (c) The recolored, (d) The simulated recolored image.
(a) An original image, (b) Simulated image, (c) Recolored, (d) Simulated recolored.
(a) An original image (b) Simulated image (c) Recolored (d) Simulated recolored.
Results of a recoloring images for protanomaly
Results of a recoloring images for protanomaly
Coefficients for recolored image
Quality factors and co-efficient for recolored image
The basketball video results. (a) An original image, (b) The simulated image sequence for protanopia, (c) The recoloring method, (d) The simulated recolored frames.
Co-efficient for quality of recolored video
Complex coefficients calculation for performance assessment
Complex coefficients for performance parameters
As these types of CVD’s cannot perceive green completely or partially confuse between green and blue color, they face problems in daily activities which involves green wavelength like choosing between green leafy vegetables and fruits, etc. not experiencing green color in multimedia or even in live events like matches. Now firstly, focusing on Deuteranomaly where the CVD confuse to see between the green color and the blue/red color completely under normal lighting conditions. The following is the algorithm for Deuteranomaly recoloring of video/image.
Algorithm:
First the color blind user accesses our website. We ask the user if s/he suffering from deuteranopia. Then we ask the user to browse the image or video that he/she wants to process We take a copy of this image as the input for our algorithm & keep the original image as it is. Then we use our algorithm to process the image and if the user take a video as input then process the generated frames. We now get the recolored image or frames suitable for the color blind user as output. We give this image as output to the user and ask the user if he/she wants to save the image or video frames.
(a) An original image of Test-Cube, (b) The simulated image (as seen by deuteranopia), (c) The recolored image, (d) The simulated recolored image as seen by deuteranopia.
The figure given above displays the result of the proposed system for Test-Cube original image (Fig. 13a). As deuteranopia cannot perceive the green color (Fig. 13b) is the simulated image created by our algorithm. To overcome this drawback, we re-colored the image (Fig. 13c). After re-coloring the person suffering from deuteranopia will be able to view green as different color recolored to brown color. This type of CVD will be able to differentiate between the planes of the cube (Fig. 13d). It is the beauty of our algorithm we are able to recolor the image as per the simulated image which is been perceived by the deuteranopia.
(a) An original image of Test-Spheres, (b) The simulated image (seen by deuteranopia), (c) The recolored image, (d) The simulated recolored image as seen by deuteranopia.
The figure given above displays the result of the proposed system for the test-spheres original image (Fig. 14a). As deuteranopia cannot perceive green color balls (Fig. 14b) is the simulated image created by our algorithm. To overcome the drawback, we recolored the image (Fig. 13c). After re-coloring the person suffering from deuteranopia will be able to view green as different color recolored to brown color. This type of CVD will be able to differentiate between the different colors of balls (Fig. 14d). The beauty of our algorithm is we are able to recolor the image as per the simulated image which is perceived by the deuteranopia.
Results of recoloring image for deuteranopia
The various important coefficients for recolored image
Quality factors and coefficients for recolored image
(a) An original image of Test-Flag, (b) The simulated image (seen by deuteranopia), (c) The recolored image, (d) The simulated recolored image as seen by deuteranopia.
The figure given above displays result of the proposed system for the test-flag original image (Fig. 15a). As deuteranopia cannot perceive green color region (Fig. 15b) is the simulated image created by our algorithm which is perceived by deuteranopia. To overcome this drawback, we re-colored the image (Fig. 15c). After re-coloring the person suffering from deuteranopia will be able to view green, our algorithm recolors to dark brown color. This type of CVD will be able to differentiate between the different colors of regions in the flag (Fig. 15d).
(a) original video frames for deuteranopia, (b) frames seen by CVD, (c) Recolored frames seen by CVD.
The description of input video for deuteranopia
Coefficients for quality of recolored video for deuteranopia
Complex coefficients calculations for performance assessment
Basket Ball video results. (a) An original image, (b) The simulated image sequence for Deuteranomaly, (c) The recoloring method, (d) The simulated recolored frames.
Hence we can see that the recoloring processing time for an image is very less like within few seconds, but for videos the process of breaking videos into the frames and then reoloring each frame is taking a lot of CPU processing type and then taking all the reolored frames and clustering them to form the original video back takes another decent chunk of time. So only in case of videos the user is asked to submit videos and then collect the recolored video after sometime.
Description of Input Video for Deuteranomaly
Coefficients for quality of recolored video for Deuteranomaly
Complex coefficients calculation for performance assessment
As these types of CVDs cannot perceive completely the Blue color or confuse between Blue and other two primary colors which are Red and Green, they face a lot of difficulties in experiencing visual media like sports (sports match between India and Sri Lanka that is teams having jerseys with Blue shades), adventurous TV shows like Man vs Wild or Blades of the River where the CVD won’t be able to perceive blue shades. Though the percentage of Tritanomaly and Tritanopia suffering CVD are very very less, yet they must be helped experiencing visual media in which they can be able to distinguish colors of blue shades and their respective type. Thus our algorithms help the above mentioned types of CVD to perceive some other color in place of blue but distinguish it with other colors and thus experience the contents of visual multimedia in a better way. The following are the steps for Tritanomaly recoloring videos and images.
Convert the video into individual frames Extract the RGB color space of a frame Convert the RGB into LMS color space The transformation of RGB to XYZ to LMS:
Transform to colorblind LMS values
Transform the new LMS values back to RGB Values
Calculate the error between original RGB and new RGB Modify the Error for people with Tritanopia Add the error to original RGB
Merge the individual frames to finally make the new video. End
The algorithm designed is better in efficiency and accuracy as compared to existing techniques.
Hereby attached are the image and video results and following them is the table which shows the processing time required for successful recoloring.
(a) An original image, (b) The simulated image, (c) The recolored image, (d) The simulated recolored image.
(a) An original image, (b) The simulated image, (c) The recolored, (d) The simulated recolored image.
(a) An original image, (b) The simulated image, (c) The recolored image, (d) The simulated recolored image.
(a) An original image, (b) The simulated image, (c) The recolored image, (d) The simulated recolored image.
Results of recoloring an image for tritanopia
Various important coefficients for recolored image
Quality factors and coefficients for recolored image
We are populating the output in the form of website. The images used in the website are derived using the above algorithms. This enables CVDs to perceive the visual multimedia, except for Monochromacy. This is due to their inability to view any color, they see only grey-levels. Thus our website will contain recoloring algorithms for the following:
Protanopia Protanomaly Deuteranomaly Deuteranopia Tritanopia Tritanomaly
The proposed website (
The welcome page of website.
Ishihara test of our website.
Normal vision result of our ishihara test.
Protanopic CVD result by Ishihara test.
Post this, on the basis of the assessment of a user’s input/feedback, the respective color blind recolor model is selected automatically and the user is asked to submit the desired video/image for recolor processing.
If the user submits an image; recoloring process starts on the server side and the recolored output image is generated within seconds and displayed on the website. The user is also given an option to download. If the user submits a video then the recoloring process might take some more time depending on the size of the video (as video processing involves breaking of frames, assuming each frame as an image, recoloring each frame and then clustering the recolored frames back to form the recolored video).
The following are screenshots of Ishihara plates test implemented in our website.
Thus in this paper, we have discussed the results of recolored images and videos of all the types of color blindness except Monochromacy. The algorithms are implemented in MATLAB programming language; hence the efficiency of getting the results is in quick time as can be inferred from the above results. Image output is still visible in few seconds, but for videos, the execution time is much on a higher side. Also the video recoloring of Tritanomaly/Tritanopia is not included in this paper as they would be included in the next paper of our research work. Also image results for Deuteranomaly and Tritanomaly are not included. We are planning to implement all the above algorithms in Python programming language to improve the output generating capacity of the website. Thus efficiency is expected to increase by 50% for videos. The succeeding paper would be the final results paper which would describe the website and results of algorithms implemented in Python Programming Language.
Footnotes
Acknowledgments
I am thankful to the Ethics Committee for Academic Research Projects (ECARP), the PG Academic Committee of T.N. Medical College and B.Y.L. Nair Hospital. Dr. Renuka Munshi of the Pharmacology Department of T.N. Medical College and B.Y.L. Nair Hospital for her guidance to improvise Algorithm Protocol and Methodology for ICD of Color Blind patients.
