thank you i and the money uh my name is that can and and i like to come to my is should issue days section they do not think my uh i'll start the a a a a a you this description of the problem that we have in this work that i i the might do some pride my one prediction using uh uh have they makes based on its and sparse estimation based methods prediction yeah know you to you are new approach based on a naked my they should next yeah direct application of you and so that no matter to which pretty i was to some experimental results that i finish my and my presentation that we've come so in this part we actually at the problem or for or close to image prediction so when we talk about a close to image prediction maybe be of the art still is that for inter prediction models so they uh this uh this prediction middle is actually uh that are in a home in is region in a an image or or or or in i it also sometimes what cost to as the orientation of the models a a like you want to so a also is okay the the interpolation fails mostly in in all complex we use and the strike so you know that can this kind of a that search is there were lots of that which and based algorithms and inc as an additional mode in a stuff for uh or or or even sparse approximation based can be used as a generalization of the that made you know a to giving this brief information my like to remind you based on for intra prediction models uh uh is you may or it did not that there are two uh two that of the prediction system by sixty L four by four and sixteen by sixty has for prediction models and for by four had my models including yeah one P C and racial i did a simple is just the way that place or simply to it's the piece of values which are already included uh a a big one be yeah in this but a space maybe paper and and we talk about that reflecting is also a very well known a simple algorithm you know uh people use the define that play very a cool and a close to B C of the target well and then and like to find is uh a in a coarse search we know and uh and that the the minimum distance between template that K yeah i a and into the allows us to to to the setting E you can they work that is just copy that they too to the piece of but using target little be pretty as an example i would like to show you the time uh house middle yeah this is just a is that an additional more in a stuff to six for and uh result in up to a level course bit-rate saving and the the idea is simple a a a a lot to be pretty the for by four block is divided into a for some some and that that meeting just uh a a a a a a light on this sub looks you know that the the the production of for for problem i don't using and some multiple but there's that everything them using a a a a a lot that like of that but it's uh a result up to fifteen it is fifty percent receiving a saving unit stuff for we simply V into into used to is sparse prediction of a sparse approximation based a algorithm so instead of matching mating template tries to combine size a a meta data uh and yeah D we calculate in coefficients which are okay you think that that's five three is fess up estimation already we selected i that used and that use the same movie i a make it is target so before going i the details of the formation i like to him just the it to in addition so so that we have now define a as so it's been no and and i got a should have a support C and uh are currently be but it is the me so i just these values in two i i uh we just like a sample values and that i we can uh the the vector don't at piece of C which is which was was the support region C and the since that it it what is that you know the order of values of the block to be pretty the P and and you put these two values and a piece so and the we we you have a and at i which is a matrix and this time the or or or the image patch in a in the court to search window and put into columns of this matrix and then be again a compact the this metrics into into a it's of C an A sub D which it's up C corresponds to you sparse it's but the location of the the support you can see and and the other one score is supposed to be the block to be predicted or one we have it done by to sparse prediction so we we have a we have a a a constraint approximation of support region you have this constraint because uh uh you try to approximate the template actually a good approximation of template uh you so sometimes we not the lead to a good approximation of the look to be pretty the so what do we do is that we we is a sparse sparse representation uh algorithm of the algorithm a greedy algorithm and at each iteration of dog within be to try of this sparse uh big doors uh think that the tuition and fee check if is uh if the if the block to be predicted to do the unknown low uh approximation is good do not and we right to my using a limited number of iterations in this uh uh in the sparse approximation but so in this case we we need to signal this this select a sparse to that which which is the optimum reconstruction of the unknown but B and that i no is just a a a a a just a by by multiplying a a corresponding matrix with the the selected optimum uh sparse sparse like so this was just the from the T to true so i would like to speak about an hour a non-negative mutts like to this algorithm is actually D B a it's a low rank separation of of but uh i i of data and it is a proper that it's it's always a naked the D and and the and it's it's very useful for four that yeah for physical the that for i interpretation of the results of the in it but but is an algorithm and and this which is are using this in a implies that action of that the mining and noise remove remote locations in other words that's up to that we are given a non-negative metrics but the matrix E and the B try to find it's medics factors those a and N and that the the usual cost function of and M actually the a it it didn't distance with the the constraints of the didn't the elements in the match this is R are non-negative always this is this is a well known problem and it's sold in two thousand by a by lee and and the it is uh uh a than at multiple multiplicative update iterations and starting with data no um randomized and non-negative a image you listen of a and T a and X and a a a at the knitting update the conditions it's true that the this the and it's good it the distance is decreasing or each iteration uh we can we can write this uh a cost function of and i in in a vector of form so that's suppose that we have a a vector B and which which needs to be a but to write a yeah i at least a a and a vector X oh it still value the yeah equations uh a real work with the with this kind of problem and are a i i do have here is to feed a and B be is actually because it's the data which needs to be packed but i be fixed at here so that me just remind you what was a a a is the T the text patches extracted from these uh this course so it's we know that so it's a a and B that they try to find that and i i've uh a representation of the support region and then the be approximate the unknown block with the same power right or but a more of for a for like this uh this iteration into a a a a a a a and so we just use a sub C and a subset which corresponds to the template and the dictionary for the template and since we we fixed T dictionary a C we have only one it to a a a i if shown that for i so this uh X it's start the on the initial right uh a non negative values we and it's it's rate until uh a it to the final iteration number or or or or or or a a condition which is that's fight by by i and uh did did the predict the values of B are used they get the it is a using D the vector vector X which is the the final iteration of four this out we didn't band the the use the the dictionary which is which corresponds to but look to be pretty a like show some experimental results these are the trivial result that your date a you to perform and and the for barbara for come amount of be test are algorithm it the input in can present to order on matching pursuit and the template matching or and uh you can see on the top of the nmf algorithm as the right the it and in terms of coding efficiency oh results uh uh for a reconstruction or for for of the first frame that we use that and the again here you you can see E D the the degree in the bit rate and the the increase in the P that of values greatly improve but not to take a look to prediction on it not be function as a as you can see the the prediction is is that was supported this is this is why the we B don't have any a constraint on the on the number of just to be used it you do sparse approximations we fix the be be value D number of but and and have it made one to one that is used to for prediction but in an F B we didn't have any any constraint so starting that this observation be just the impose a sparsity constraint on image and a a constant is just to L O K K I just K non-zero elements in the sparse vector and it again can keep track of these sparse vectors to what my prediction as in sparse approximation but and if we if a again from a like this it a similar to sparse approximation algorithms but prediction algorithm excess we have a non negativity constraint on the on the corporation and of course you data and that that do signal you the the value of K select to optimize uh of the number of by and the the to the prediction is a to i a the the signal was that that they can in the same manner as the as a sparse prediction matter so here really since we used that used the computational load because of the sparse the constraint we we decide to include use instead of using one the one template we we we introduce minor models to to select the best one as in it is to you know to compare with they stop to six for because they stuff two six four four by four intra and by modes so we just decided to have nine minutes and the compare with they started for prediction and and and here uh since the V set a well these step this we need to signal it as an integer value to the so i would like to show you region which is extracted from for an image uh and it's very low bit-rate prediction so you can see this is a step to six for prediction and sparse approximations and the sparse nmf algorithm prediction methods and you and you can see D the artifacts on sparse approximation on the age and the and in and uh there is no facts on the predicted image and the uh a image which function from of from by about uh and uh in that it takes to region and can clearly see improvement on the visual quality at least the it's improved by by this algorithm uh final of a are T i a compression compression results are which are are compared to a step to six four and uh and a a sparse approximation and them man for about about five four real images or B we just a to it is the sparse approximation at times and B cheap i J X uh i i'm sorry to as a for uh or nmf algorithm so it's just that a two one to eight to the number of buttons a varying from one to eight and is for by four block size and based prediction is selected by a a a a a function so are the top and the road that curve is and F and uh the blue blue curves are a corresponds once to a course one to sparse possible was approximations that we first course when they that for once to prediction modes so the conclusion in this work we just the introduce and no image prediction mid which is placed into in but at that time instant the detection method algorithm and it's the constraints it even rubs better and this can also be a to to image inpainting what was lost can and applications and there is a final remark a it can be this all them can be an effective alternative and the but it's compared to other metrics as like this guy before and this this presentation and i would like to thank you for your time and you have some questions i would we happy to that's but i have a questions you a is group sessions or just step up to the come from i have some questions or thank how is the computational cost to the other math uh the the computational cost compared to a step for is uh it's high because in the in a is that was used four it's uh the the P there's the interpolation prediction there's are defined before and just the they use these few into a in in in into the algorithm to to interpolate pixel value uh but that's why of them work for for texture regions and complex start so you know to based technology those so to two to them a some complex algorithms to uh to overcome this lacks in the in the image operations so yes i yeah you in terms of complete uh in terms of computational complex the and compare it is not to for it's higher than it's that is for but it's sparse approximation all uh it's it's as the same i can see a question i so when you are as your uh a questions for the yeah and i'm at a good sparse the or are you have you at that at the for a for our three oh a a a a that the sparse representation for a similar except for that you have a constraint to X square we equal to is you know but there why do you think that you met i or if you put the constraint okay okay question actually uh uh in sparse approximation method if in each iteration you try to approximate the template but it it the post iteration you find the the highest correlation between the we in template and the atoms in the dictionary and then you get it is usual and that at the second iteration you to right you you you process on the residual image it is usual of the template okay and you know in a in a in in the special domain the template and the the unknown block are correlated to each other but in the residual the they are not correlate so first of all the that that's why uh for example a like a quick meeting coefficients and sparse approximations might be very good for template but it might be very very uh you know uh i E it my a can it might contain a high frequencies for for the look to be pretty so since you you try to uh i could just edition just you use but was used but probably the patches instead of instead of using correlation uh correlation be a correlation coefficients which are a it on the residual domain and if you see we don't uh we don't use of the residual information in nmf algorithm be just use the patches which are very close to apply uh i i i hope is it's clear oh the steak house