among everyone this is a of lossy compression on a lossy compression on a set for hyperspectral and all spectral images that's a joint work with and they are about the not a button you the university of C N so first give some motivation apart one but was a compression and the a specific problems i will describe the proposed compression algorithm then a provide some experimental results on i spectral and out for spectral images and finally draw some calm um i spectral images are a collection of pictures taken at seven different wave of the same scene quite a lot of data but when it comes to compress those data on were set to like we're faced with the problem that we don't have many computational resources to actually do impressions so first that the need is some low complexity um that's quite different from compression on the ground our car idle is the second but it thing is that unlike typical consumer digital cameras where there is a two dimensional detector are rate to picture for hyperspectral images to we just have one um a signal one dimensional array that takes one a line of the image with all it's a spectral channels all the different wavelength the i-th the lines of the images or the image are for by the motion of the aircraft or set light so it they are taken at time so that what use in table is that's we to the compression we don't have the whole image available but just a few lines of the image with all the spectral channel so we need to do compression using a a we will about one so that's do we want to compress good as we can so we want to have state-of-the-art compression efficiency and for hyperspectral applications we need to cover a range of bit which is relatively large to be from around zero point five two three three or four bit a pixel that should be compared with a a sensor be that is typically between twelve and uh sixteen bits per se so we have to cover a a little bit rate and high bit rate fashion and finally we need some error containment uh those uh compressed data packets are down only uh use a communication channel which is subject to occasional back loss and we don't want that's a the signal back to these disrupts the reconstruction of the whole so uh there are several of all buttons for performing compression of two dimensional images like and hyperspectral or on for spectral images a most popular approach uses three dimensional tile from coding for each sample jpeg two thousand we part two with that of course a multi component transformation where you can use you the spectral wavelet transform or an arbitrary spectral transformation and then the light jpeg two thousand on each of the transform a a spectral chan we we can use a way list we use the couple nonlinear transforms whatever this uh uh was very well at low rates of the problem of this approach the high complex complexity comes from the top for but you know or or from the coding and rate distortion use a shouldn't G a thousand where you had to uh do we i i coding all you links and then post compression rate distortion ascension so that mass too complex for on more that it does work well for archive hmmm the second thing is that if we want to use J two thousand well then a for compression we don't have a whole image available so our spatial transformations we have just to take that on a few lines of that image at a time which is that a possible with J a thousand using be line based transformation but then that start from them a shouldn't can be done in a global way more are you has to be done and a lot of weight just a few lines at that time and there's a big performance pen or that oh to be a a global optimal rate distortion age the you the also approach is to use of prediction techniques using three dimensional spatial and spectral prediction but action has been used for a time for the last this compression yeah was this compression used to to go for high quality applications where you want the maximum absolute error between the decoder in an original image to be a by a a user select value so that works very well i high rates i it doesn't work well as well the little bit rate and then uh a two dimensional prediction is usually a couple of colour this station an on how one i entropy coding have a a clear that we don't go below one pixel the short course a code word but how one on can provide is just one so uh that's a problem to go below one so uh what we propose for a compression is based on a a approach where use three dimensional spatial and spectral predictor or which keeps us a low complexity that need a compression but then we're faced with the problem of improving performance of a low bit existing schemes the just don't them so that we don't know how do them is she's we don't really need to perform you was let's compression move to truly lasting compression all the prediction residuals and so in order to do that we improve quantisation stage we don't use a simple scalar want either and we a rate distortion optimization the whole ski so uh this is how we do it uh the prediction stage you use therefore forms the prediction independently on sixteen by sixteen blocks of samples so the speech or shows uh and in which dividing in four sixteen by sixteen blocks and uh uh of for every we look at a all channels and the court look blocks in the the different spectral channels so looking what where the wavelet to mention we probably right from the curve at decoding block the bleeding spectral chan so this is quite different from the kind of prediction that is usually uh and blowing in hyperspectral image compression pixel by pixel and not a lot by block prediction but as will see is a loss as to a very efficient to start from i so essentially what we do is uh that are would need to in the next slide that we calculate a linear predictor that use the previous well but it be current law then we calculate the position the prediction residual and i think about this is that it provides a spatial error containment in that it some compressed where one is lot should that we have set the next blocks in the uh the the the the block in the same spatial position in the next wave lines but it not affect and now their spatial loss which so the prediction is actually quite seen but we got X the vector of samples of the current sixteen by six in lexical that ring are the vector of samples of the reference plot which is as i said but score okay at decoder block in previous spectral channel and then we calculate at least mean square predictor or with which is defined by two parameters are are fine and and and is the mean value of the current block and i'll wise at least means square parameter that pretty P current uh one probably principal alright so uh the first uh the other thing to move formula lossy compression quantisation uh uh technical near lossless compression of less quantisation which is almost a do not at high bit but far from optimal a low bit so for a bit rate compression its customary to use one with that some which creates long sequences of zeros that are back to the effectively by and three coders is is the optimal at lower rates not i rates and to find some that works well at all rates we decide to you a a kind of quantizer which is called uniform threshold quantizer or or you Q which is slightly more complex than E in form quantizer but dead zone but is the or not at all yeah O you do you is actually very simple it's i i i there in which every interval what decision you of rows are all the same size so calculating the court were is done much the same way classical colour you from quantizer the difference lies in the fact that that construction there is not taken as the midpoint point of the quantization interval but rather the sense as H so uh since we are a blank these to the prediction was walls we make use some so that the position was you those for a two dimensional a a two sided exponentially decreasing distribution lower plot distribution and so we calculate the uh actually actual construct and that used as a it's using these is to be and what happens is that if should look at this speech or so we you can see here are are are the different quantization interval the getting but of the point of the interval that you know a construction put by a a or for one times are a seems we use this uh we make this some some of distribution their out that the way that is of the prediction that or or more be then the high values so uh what we do is we add the collection term to the the construction but account for that so that by is that construction to works a little bit so much so as the uh quantisation indexes low so close to so that uh the or from your last and the second and was the most important one is rate distortion optimization and this is where really helps to use a square blocks for the prediction so the eight yeah here is uh essentially similar to the skip mode be the compression sometimes we find certain sixteen by sixteen or that can be pretty to really very well from the you reference blah and in that case we don't in the prediction a so we are other keep the encoding of the prediction or so that we save a lot it's in the process and just signal to the decoder that the decoded signal on this case we just the pretty that the decoder can a couple in particular we actually prediction we calculate the variance of the prediction and D and he can they're this variance for the special and if D X is the threshold that it means that the predictor is not a good enough for speech in the current block so we don't the classical and encoding of the prediction or i rice if the D is below a actual we simply to that and prediction parameters for a lot of the file but no prediction that so so that the the would will be a a to uh mean the petition parameter from the file calculate a pretty or in use the predictor as coding entropy coding all B but addition was used was is done using a a power of two call this is a a very typical become uh thing in uh compression for set like imaging because goal of two codes are much see there than any other a a cool especially arithmetic medical so uh there not was powerful but that that's of a good compromise performance and complexity and calculate the best coding parameter every sample based on the of magnitude of on my prediction residual of a window of thirty two so it's not done a lot well a sample by sent right so here are some results for the proposed algorithm a a tried that on a different images all show results for images from two different data well as i've is i have this is an images using spectrum there long which is flown on the aircraft and these images are had it they have two hundred fifty four spectral channels and the spatial signs six and an at times five hundred and twelve images um and the is a the right images as are where by this they have no calibration whatsoever no of corporations and not with really image it is taken by i've of is used for so that classification of locations and oh the second image isn't a in image from the years and some the which is operated by the not which is used for a static of studies these images have a a a a a much less piece spatially just one hundred and thirty five times ninety pixels but they have a a spectral channel signal can hundred one spec is a quality metric we look at the P peak signal noise ratio and and we compare the performance of the proposed algorithm with two other algorithms well i he's jpeg two thousand part with the spectral discrete wavelet transformation in this case we do not perform the three dimensional a rate distortion optimization we're not doing any line based comes from so that is also be shown for J a thousand or and i'm realistic and one would be actually run the set a sort of upper bound of the or of J and the second algorithm is near lossless compression use exactly the same predictor and it to be colder and not using the U G Q quantizer or nor the latest store just a or a by E D P C and we discard uniform quantization and entropy coding of the prediction was are the results the curve here july two thousand to the wavelet transformation and a continuous list compression algorithm it is no you assess compression is better and transform coding at high bit-rates and you can see that here will performance difference with respect to jpeg two thousand speech large over two bit per sample but that were try this this is not a as good uh so essentially for two reasons one is related to the fact that the like to the quantization step size and the were is the quality of the reference signal for the prediction so these points this brings the performance style and low bit but then and this i have is not able to achieve a rates be more one bit a pixel because we're using a got a call was mean got were like this one there's just no way to go below that the proposed algorithm seems to bring the best of both worlds here better then a job but two thousand and and are a bit rate is larger than then point three or do you point thirty five percent so you and that's bit rates the rate distortion optimization works pretty nice here and it's for for its performance tends to the performance of the yeah lost this compression at high rates and that's reasonable about because at high bit-rates that it is a shall never select the skip model for any block the image and a uniform threshold quantizer tense this colour one so the two algorithms essentially become pretty much the same we have similar you a results for the it's image sort of a yes do a two thousand as a little bit better or sometimes are performs by a small market proposed algorithm some but is not quite as good essentially you know a comparable performance and and that's pretty much the same you some near as compression is not a as low bit rates is become pretty high bit so this is still a a a a lot of jpeg two thousand for this image but jpeg two thousand you and recall is using a um the from a three dimensional very from as a so if we use the line based on from no one so um alright right so uh the this is an example of visual quality and this is just essentially sensually goes to show that we all the were using a block based pretty or we we don't have any hard here so this is a uh a patch from one the end of every as your original signal and this is not a construct signal by the proposed algorithm at zero one forty bit per piece so it's is you know one of the oh well as bit rates at the output but in can achieve and as can be seen that i mean the artifacts but no not not to science what's that the is is that a lot not the facts uh come from the quantisation the transform from the a some from the coupling of one position first transformation where i is in this case where using a block based pretty but the quantisation used and independently of the signal send pretty and what not so this is what would have a job but for example which creates you know a a lot here just which essentially keeps the text or alright right so uh you can can an uh the proposed is essentially uh a a a and you by for compression of a a hyperspectral image where we achieve lower complexity by use a prediction based approach which uh uh forms uh is known as or better than the state-of-the-art of the art three dimensional for coding with really feature distortion for optimization so that seems to be a nice way for for on what compression of set images complex in memory requirements are significantly lower than jpeg thousand a it's difficult to compare the complexity of different algorithms by top to sign this working on J two thousand and seems like the proposed approach to be like and man two fewer operations than J for a to the same on this uh but used in room for improvement we're not using any or i've made calling but that and certainly have the coding efficiency apartment coding what most are as by some margin we might use know and of the ring that is using for a reference uh spectrum channel for the prediction not just a three spectral channel a the spectral channel but use more correlated with the colour channel so this is especially on is that provides the nice performance uh this algorithm is people proposed to the european space efficiency is in is of a mission for the spectral image image or these it on the is i X amount for is going to fly to mars you that do have any questions i can you can make any comment in regards to um have the compression technique might affect processing that would occur after uh the images uh transmitted for example yeah end member extraction or some sort of classification task yeah uh that's something that wouldn't propose lossy compression to a remote sensing device the re scared about the potential negative effects of lossy compression so uh we we were that experiments in the past with that's and my feeling is that if the mean square error so you have several quality metrics cd can to measure that not just mean square error the maximum air spectral and will and and a lot of the matrix but my experience with that a if the mean square error is low in a a small have then everything we were very nice and it's uh in for for this kind of missions you definitely want to uh to keep the is got a sufficiently small uh for not a hyperspectral image but for a spectral uh says i'll um existing since C systems actually use a compression spurt five does use lossy compression at a bit of well i think a three per pixel from paid and the can a set of in just lossy compression so uh uh the government agencies which are using problem funding they don't really not care about a lot of compression but people that the private companies they they care of what's so my feeling is that compression is not a big deal uh are exceptions obviously if the mean square error is is small enough run problem uh comes for example from applications like a novelty detection where a a large at so the one on one single pixel can actually by the result of a normally detection so one has to be to have a in some ways but for classification my feeling is that a more less goes we the mean square error if the means got is low we have time for a a question quick "'cause" take a speaker