alright one two like yeah well yeah part results you got your in our studies on some so we can practise having shoes in image that's an and more precisely about that and duration based what there's which are one of the most successful quarters during the fast decade uh any better based coders we can see containing these general steps yeah first by applying development transform of the input image the coefficients remit is generated and then from these coefficients on the lower one regarding the current budgets and limited partners together with their locations will be coded transmitted to the decoder using these coded location uh locations of do what they're we'll be able to regenerate the remit to reconstruct the image by an inverse transform on the decoded quite efficient or is based on the conventional are separable wavelets piece to horizontal and vertical directions software from a non problem and the problems around the edges if you can see there and it's like what we have here information independent the spanish uh sprays across many coefficients and this means when we have limited budgets some of these are significant coefficients cannot be coded and that reconstructed image may be some regularities around it our remote that yeah and uh then to detect the menu research has been done to overcome this limitation and all a wavelet transform on image edges uh but it's just some of them are the an isotropic dictionaries of care less control this she speech uh i have of saddam of in terms of approximation power because of the year a high redundancy can present is not between one of the most successful the application reported on these dictionary uh we also have a that that's transform which becomes optimality similarly uh forty presenting in it but the code there's based on the visual transform be the L E so that image geometries stones exactly match the base your geometry which is the case at the textures for example and to make it also at the cable for these case says as there are are some words that combines the base that's transform be to be a web based coders and finally we have that i do not apply in be let's of long those durations duration of a list uh this leads to several the schemes such as man this direction this and many more that's decide just some of that uh if we only in you were taught that it off these i do not and for image compressed and using a simple model on it if we have right but along the this direction forty five degree direction and then and the second stage of not six to seven and all the are in the case of low how bad has then for a high pass band and no ninety degree or any other directions scenes yeah does happen dominant and direction uh don't don't that have process usually a uh result in using and signal it can see the in should off or the three are group for a a high frequency bands and uh and so on like the separable wavelets here uh no not not the most variational image and the image and this is of great importance for comparison purposes and it's uh improves the compression results yeah that's in terms of psnr and also visual quality and a as you can see that the you yeah or the ringing artifacts around the H is the same in this up here and the results uh i yeah function list uh when we move into two images these it is a lot more than one direction uh we should experiment the image into some homogeneous partitions and and assign directions pair partition yeah we have a popular partitioning algorithm which is quite three you you and can see that the as a distortion in court image or images to decide between on and its construction i had and also or and the number of bits we need to go to partitioning a structure and the selected directions that quite cheap follows the query all uh are reducing the or not that original cost function which is a D plus them do or in an iterative manner a this means uh using or or a model of the gone first the but with is up a lot of the best direction and D plus them or is computed then it uh a you made use is split it into four forty to all blocks and for each of them to same the steps of a direction of late a "'cause" is perform and uh finally be uh make a decision after splitting the original block if the news a lot totally reviews the define a cost function if we uh these a for each partition as long as he can improve the plot them or cost you can see here there is also four levels of what she on the polygon model and after these you yeah about the questions we personally during or studies pairs we want to know are we can analyse that i'm very successful direction of a list uh for a man so critical point of view you in image compression and also was the results of to marry direction and separate weightless assume basically as another question we want now well actually provides an optimal partitioning for duration for these we or analysis on the don't know rate distortion framework which is treated as a comparison there was used and at least framework if you quote the impulse function if using or then the performance of the coder decoder uh pair on the input function a space is equal to the maximum distortion being mapping yeah to the that's that's true for all elements or man from its it is well to this optimal or of the distance between and it's reconstruction um we do a or analysis on a a classical piecewise linear uh yeah it just um a smooth transitions between zero and one regions and first if you can see they're just one place without any singularity we that's for increasing or yeah uh the distortion of uh separately liz on these uh functions we can be too large or power minus three second which is very slow regarding this in a function which can is decoded by a few problem uh we then to that direction is be headed financially and it is very faster than the support of this also confirms the uh improvements you are seeing the reconstructed i mean uh returning to the whole class so that piecewise functions is obvious that the performance of separable wavelets will not change because okay we use the same horizontal and vertical directions for all cases in the case of duration of it was as we explain for michelle partition and i if we use quad tree then be who that again direction and the list for rice and exponential rate of decay but is somewhat this slower than the one piece case by changing the Q a this work of top or into to the Q or or at the exponent and also introducing a six a put a four factor and what we D grace the performance of a duration of the a list because two oh only present a singularity below it generates many blocks this similar direction that a lot of those should be called a this you impose a large amount of overhead on the algorithm just a thing is an issue you want to use the idea of joining similar blocks and creating larger lots that called make a locks and if we clara duration yeah directions per macroblock then in addition to better representing the image geometries here we have your reviews the amount of over a signal we can see and finally well that's what i'm equivalently better performance art direction of the atlas will remain is added to our minus square root of or even when we have singularity is around it oh theory is and now the question is now a channel or megablocking id also be uh applicable for real cases yeah things change the reader sees you when a small amount of noise may cause what generates many different directions and these people probably you raise the performance of may be lucky and two sort of the use problem we have to modify the directions between joint the blocks uh a to do these uh uh the point is uh a large B uh dominant and edges as you can see a if you are are the ones that we'll this affected by noise and we sure chip their directions on changed and uh where where and about when the loss it a a smaller uh the noise can miss lead they with them to so like the or to select the wrong direction also uh at this smooth regions because of the random ms nature of the noise B would have many different direction active to blocks these uh or or the ones that we want to um on a flight or we want to modify according to the you uh strong neighbours to get a larger make a blocks using this in a practical results these are steps or or off or a mega blocking them first partition image by what three of them give or wrong direction then we align these directions gets then use the top directions by see that gradient cost function of each it's first there is a measure of the detection domain the base here or the difference between the cost of that is the worst directions and and the second this indicates that the future direction of a should the most affected the what is larger and and using at the few your you can see that um of line directions of the polygon more and i here is the time that's we can joint similar blocks we've a similar directions and create make a lot uh as the second as an ex that we log duration of a list using that scheme finally record all the wavelet coefficients to be the all over this off joining but that's the macroblocks and also as the selected duration a couple of these um of uh more importance in two cases the first case is of its trace each year in terms of psnr for a run they make a lucky improves the what tree based direction of the way list i one point point one D V and the J two K bye a point six db on average also in the case of the not the mega blocking performs forms bits so that the what tree by one T V the second case these for the the and noisy images and D V can see there the gaussian watch it's is that make of looking for a phone based so that and the what tree by a one point two B in the case of our uh okay so we know why one point one db and points one two yeah as you can see A J two K um shot of the noise sensitivity in terms of psnr from the a visual quality point you it's obvious that the middle lucky is that the image you yeah that these much better these are the results all reconstruction from the three D J two K watch i a with them for the noise you being on or point one two a they're X uh all i mean these you we sure that uh the the it he's also or that that the direction a better that is that in compressed and also or make of lucky improves the direction makes it more as like it for coding be images also as or future direction we all uh i be or that E and i Z i i duration as for working on or data yeah