 Hello friends, myself, Darshan Pandit, assistant professor, department of computer science and engineering from Walchand Institute of Technology, Sohlapur. So, today we are going to discuss about anti-alising techniques in computer graphics. So, the learning outcome is at the end of this session, student will be able to minimize effect of alising using different anti-alising techniques. So, in this we will see introduction to anti-alising and different techniques, those are increased resolution, unweighted area sampling and weighted area sampling. So, in line drawing algorithm, we have seen that all rasterized location do not match with the true line and we have to select optimum raster location to represent a straight line. This problem is severe in low resolution screen. In such screen, line appears like a stair step or jaggies as shown in this figure. So, in this diagram, you can see the V is represented, so when you zoom this V, you can see this jaggies more clearly, so which looks like a stair steps. So, these stair steps or jaggies effect is known as alising. So, the same thing is shown in pixel representation in the below figure with the alising effect. So, first row you are having 2 pixels, after that 3 pixels, again in third row you are having 3 pixels, in fourth row you are having again 3 pixels. So, this is how representation in a pixel format. So, the alising effect can be reduced by adjusting intensity of the pixel along the line. So, this process of adjusting intensities of the pixel along the line in order to minimize the effect of alising is called as anti-alising. So, the first technique we are having that is increased resolution. The alising effect can be minimized by increasing resolution of the raster display. So, the figure A shows the normal image with the lower resolution screen and figure B shows increased resolution screen. So, by increasing resolution and making it twice the original one, the line passes through it twice as many columns of the pixel and therefore, has twice as many jags. But here each jag is half as large in x and in y direction. As shown in the figure, the line looks better in twice resolution. So, but this improvement comes at the price of quadra coupling, the cost of memory, bandwidth of memory and scan conversion time, thus increasing resolution is an expensive method for reducing alising effect. So, you can see that is unweighted area sampling. So, in this we have seen that for the sloped line, many a times the line passes between two pixels. In this cases, line drawing algorithm selects the pixels which is closer to the true line. So this step, the line drawing algorithm can be modified to perform the anti-alising. So, in anti-alising instead of picking closest pixel, both pixels are highlighted. So, however, the intensity of the pixel may differ. So, here in unweighted area sampling, the intensity of the pixel is proportional to the area or the amount of the line occupied by the pixel. So, this technique produces noticeably better result than the setting pixels either to full intensity or to zero intensity. So, in the diagram you can see, so the red colour is nothing but our area of interest and so we have added the equal intensity pixel above and below this area of interest, so that we can reduce the alising effect. So, in this unweighted area sampling, equal area contribute equal intensity that is the top and above pixel and this below pixels are having same intensity, regardless of the distance between the pixel centre and the area. So, here only total amount of occupied area matters, so this is nothing but unweighted area sampling. So, in this diagram you can see, so the all pixels are having same intensity. So, if you leave this true area of interest that is true line which is marked with the red colour, then all other pixels are having same intensity that is in unweighted area sampling. So, thus a small area in the corner of the pixel contribute just as much as an equal area sized near to the pixel. So, both are having same intensity. So, to avoid this problem even better strategy is used in weighted area sampling. So, in weighted area sampling equal area contribute unequally that is a small area closer to the pixel centre has greater intensity than does one at the greater distance. So, in the diagram you can see, so this is the actual area of interest that is the actual true line which is marked with red. So, in this you can see the pixels nearer to the true line are having higher intensity. So, when you move away to the true line the intensity get decreases. So, that is what equal area contribute unequally. So, the intensity goes on decreasing so that you can view the actual true line with the anti-alising technique that is by removing the aliasing effect. So, here in weighted area sampling the intensity of the pixel is dependent on the line area occupied and the distance of the area from the pixels centre. So, this gives them better result that is weighted area sampling gives better result than unweighted area sampling. So, here you can pause the video and answer the question in which of the following technique equal area contribute equally? A is weighted area sampling B unweighted area sampling. So, the answer is like unweighted area sampling where equal area contribute with equal intensity regardless of distance between pixel centre and the area that is area closest is having high intensity and the area which is far from pixels centre is having the same intensity that is both pixels are having same intensity in unweighted area sampling. So, these are the references which are used to create this video. Thank you.