remote   assessment of emergency 
 medical events using telemedicine systems is likely to be a critical and 
 pervasive component of future healthcare systems . depending upon geography and 
 economics ,
remote assessment is likely to occur over both   wired and wireless 
 networks , and thus over a broad spectrum of transmission bit rates ranging from 
 very low to very high .
telemedicine systems that have the potential to include 
 video when appropriate will provide specialists with significantly more information 
 for assessment , diagnosis , and management than will systems without that 
 capability .
bit - rate limitations often require that the compression ratio for 
 video be larger than a certain   minimum value . on the other hand , for medical 
 video to be of diagnostic quality , the compression ratio must be smaller than a 
 critical maximum value . in other words
, it is essential for clinical 
 acceptability that the compression process does   not lead to misinterpretation 
 because of a diagnostically relevant loss of detail or the introduction of 
 visible artifacts .
a number of compression schemes that attempt to ensure video 
 of diagnostic quality have been reported in the telemedicine literature .
the 
 remainder of this section describes the state - of - the - art , with each new 
 paragraph describing a unique literature domain .
high - bit rate local area network ( lan ) infrastructures for 
 telemedicine systemsstamford et 
 al .  
address high - speed , high - quality video transmitted from a rural 
 emergency department ( ed ) to a major medical center ed .
the results document 
 improved diagnosis and treatment , as well as improved confidence levels among 
 doctors .  
kofos et al .   studied 
 telemedicine in pediatrics using a broadcast quality real - time audiovisual 
 system and concluded that such a system may have dramatic implications for 
 providing pediatric specialty and subspecialty care in underserved areas .
yoo 
  reported the design of an mpeg-2 video system running at 30 frames per 
 second ( fps ) and requiring 1.56 megabits per 
 second ( mbps ) to deliver a spatial resolution of 640  480 pixels . at the peak 
 bit rate , this corresponds to 0.434 bit per pixel ( bpp ) .
qiao et al . 
 addressed the design of a critical care telemedicine system based on video over 
 broadband networks .
wang et al .   described a web - based videoconferencing 
 system that allows specialists at an academic medical center to evaluate stroke 
 patients at a rural facility .
each of these systems describes a fixed patient 
 at one location connected to a fixed physician at another location through a 
 broadband connection . however , rural hospitals frequently have neither the 
 equipment nor personnel infrastructure necessary to support high - bit rate 
 broadband networks .
address high - speed , high - quality video transmitted from a rural 
 emergency department ( ed ) to a major medical center ed .
the results document 
 improved diagnosis and treatment , as well as improved confidence levels among 
 doctors .  
kofos et al .   studied 
 telemedicine in pediatrics using a broadcast quality real - time audiovisual 
 system and concluded that such a system may have dramatic implications for 
 providing pediatric specialty and subspecialty care in underserved areas .
yoo 
  reported the design of an mpeg-2 video system running at 30 frames per 
 second ( fps ) and requiring 1.56 megabits per 
 second ( mbps ) to deliver a spatial resolution of 640  480 pixels . at the peak 
 bit rate , this corresponds to 0.434 bit per pixel ( bpp ) .
qiao et al . 
 addressed the design of a critical care telemedicine system based on video over 
 broadband networks .
wang et al .   described a web - based videoconferencing 
 system that allows specialists at an academic medical center to evaluate stroke 
 patients at a rural facility .
each of these systems describes a fixed patient 
 at one location connected to a fixed physician at another location through a 
 broadband connection . however , rural hospitals frequently have neither the 
 equipment nor personnel infrastructure necessary to support high - bit rate 
 broadband networks . telemedicine systems over wireless networksbecause of the limitations due to 
 fixed locations , a number of researchers have explored telemedicine systems 
 over wireless networks in order to address mobility and the resulting 
 infrastructure issues .
discuss a telemedicine system design 
 using a wireless local area network ( wlan ) .
encoded video is transmitted with 
 ( 352  288 ) and ( 176  144 ) spatial resolution at 30 fps , requiring about 384 
 kilobits per second ( kbps ) .
this bit rate falls well within the wlan capacity , 
 but because bit rate is shared with other network users , the bit rate per user 
 varies both with user number and time , with video quality often dropping to 
 levels that create problems for physicians analyzing critical real - time patient 
 features .
chu and ganz  
 describe a mobile teletrauma system based on wireless networks where video at 225 fps with 
 320  240 resolution is used .
the network providers advertised a 153 kbps bit rate , 
 but in fact the average available bit rate was only 5060 kbps .
thus , in 
 these types of systems , limited and varying bit rate availability are critical 
 issues that are not compatible with clinical uses where the video quality must 
 be maintained at a high and consistent level throughout the encounter .
because of the limitations due to 
 fixed locations , a number of researchers have explored telemedicine systems 
 over wireless networks in order to address mobility and the resulting 
 infrastructure issues .
discuss a telemedicine system design 
 using a wireless local area network ( wlan ) .
encoded video is transmitted with 
 ( 352  288 ) and ( 176  144 ) spatial resolution at 30 fps , requiring about 384 
 kilobits per second ( kbps ) .
this bit rate falls well within the wlan capacity , 
 but because bit rate is shared with other network users , the bit rate per user 
 varies both with user number and time , with video quality often dropping to 
 levels that create problems for physicians analyzing critical real - time patient 
 features .
chu and ganz  
 describe a mobile teletrauma system based on wireless networks where video at 225 fps with 
 320  240 resolution is used .
the network providers advertised a 153 kbps bit rate , 
 but in fact the average available bit rate was only 5060 kbps .
thus , in 
 these types of systems , limited and varying bit rate availability are critical 
 issues that are not compatible with clinical uses where the video quality must 
 be maintained at a high and consistent level throughout the encounter .
nonmedical region - of - interest- ( roi- ) based systemsroi processing is a potential 
 alternative to address the constraints imposed by limited and varying bit rate 
 systems .
roi processing refers the allocation of a greater bit rate per pixel 
 to the roi as compared to the background ( bkgrnd ) .  
wong and kwok   proposed a compression scheme for 
 wireless channels where the channel bit rate is computed , and both roi and 
 bkgrnd areas are treated with different and varying compression ratios .
the bkgrnd 
 is dropped altogether when necessary . when the bkgrnd is dropped completely , 

the best possible roi quality under the circumstances is achieved , but 
 physician experts then point out that the problem of context arises . our 
 discussions with collaborating medical college of georgia ( mcg ) physician 
 experts revealed that while the roi must be of diagnostically lossless quality for a clinical decision to be made 
 using the video , simultaneous background context , or overview information is 
 also required .
they argued that background context , even of lower quality , is 
 better than a black background .
further , if the consultant is providing 
 care - related instructions to a clinician at the remote site who is actually 
 handling the patient , the consultant will want to observe whether the correct 
 manipulation is being made while simultaneously observing the patient response 
 in the roi . to summarize , dl quality in the roi is necessary and is the most 
 important issue , but is itself not a sufficient condition for the overall 
 assessment of the patient .
the maximum bit - transfer strategy assigns the highest 
 compression level to the bkgrnd , and the lowest possible compression level to 
 the roi that does not result in exceeding the overall available bit rate .
this 
 joint bit - allocation method allocates bits to the roi and bkgrnd based on the 
 size , motion , and priority characteristics of each region .
roi processing is a potential 
 alternative to address the constraints imposed by limited and varying bit rate 
 systems .
roi processing refers the allocation of a greater bit rate per pixel 
 to the roi as compared to the background ( bkgrnd ) .  
wong and kwok   proposed a compression scheme for 
 wireless channels where the channel bit rate is computed , and both roi and 
 bkgrnd areas are treated with different and varying compression ratios .
the bkgrnd 
 is dropped altogether when necessary . when the bkgrnd is dropped completely , 

the best possible roi quality under the circumstances is achieved , but 
 physician experts then point out that the problem of context arises . our 
 discussions with collaborating medical college of georgia ( mcg ) physician 
 experts revealed that while the roi must be of diagnostically lossless quality for a clinical decision to be made 
 using the video , simultaneous background context , or overview information is 
 also required .
they argued that background context , even of lower quality , is 
 better than a black background .
further , if the consultant is providing 
 care - related instructions to a clinician at the remote site who is actually 
 handling the patient , the consultant will want to observe whether the correct 
 manipulation is being made while simultaneously observing the patient response 
 in the roi . to summarize , dl quality in the roi is necessary and is the most 
 important issue , but is itself not a sufficient condition for the overall 
 assessment of the patient .
the maximum bit - transfer strategy assigns the highest 
 compression level to the bkgrnd , and the lowest possible compression level to 
 the roi that does not result in exceeding the overall available bit rate .
this 
 joint bit - allocation method allocates bits to the roi and bkgrnd based on the 
 size , motion , and priority characteristics of each region .
proposed a 
 compression scheme for 3d medical images where roi is coded with lossless 
 compression , and the bkgrnd is coded in a 
the method was tested 
 on computer tomography ( ct ) images of the human colon with the roi being the 
 diagnostically important colon wall .  

gibson et al .   implemented roi detection and bit allocation in a compression 
 scheme for angiogram video sequences , with rois containing the coronary 
 arteries of key diagnostic importance .
gokturk and gibson techniques [ 11 , 12 ] 
 are proprietary approaches that do not relate to any particular video standard , 
 and only pertain to medical data that have very low frame rates .
they are not 
 applicable to higher frame rate video which is the scope of our current work .
the 
 drawbacks of these roi efforts [ 9 , 10 , 12 , 13 ] lie in their arbitrary choices 
 for roi and bkgrnd compression ratios .
diagnostic quality is not assured even 
 within the roi because of this arbitrary assignment . in the medical domain , gokturk et al .   proposed a 
 compression scheme for 3d medical images where
the method was tested 
 on computer tomography ( ct ) images of the human colon with the roi being the 
 diagnostically important colon wall .  

gibson et al .   implemented roi detection and bit allocation in a compression 
 scheme for angiogram video sequences , with rois containing the coronary 
 arteries of key diagnostic importance .
gokturk and gibson techniques [ 11 , 12 ] 
 are proprietary approaches that do not relate to any particular video standard , 
 and only pertain to medical data that have very low frame rates .
they are not 
 applicable to higher frame rate video which is the scope of our current work . the 
 drawbacks of these roi efforts [ 9 , 10 , 12 , 13 ] lie in their arbitrary choices 
 for roi and bkgrnd compression ratios .
diagnostic quality is not assured even 
 within the roi because of this arbitrary assignment .
telemedicine systems aimed at achieving diagnostic level qualitya few studies [ 1419 ] have 
 specifically addressed the quality of medical images or video in telemedicine 
 systems .
martini and mazzotti   address the design of a video compression and transmission 
 system for medical applications based on metrics such as 
 peak - signal - to - noise - ratio ( psnr ) .
psnr , however , is a mathematical formula to 
 measure the amount of compression in a video and has no definite relationship 
 to diagnostic quality .  
ashraf and akbar 
  demonstrate diagnostically lossless ( dl ) medical images by using lossless 
 coding within the roi , but this can not be extended to video because of bit - rate 
 limitations .
implement roi compression on angiogram video 
 sequences and verify that their results are of dl quality .
however , this 
 outcome is incidental and not actively pursued as part of the algorithm design . 
 in summary , the authors are unaware of any work in literature within the 
 context under consideration ( wireless networks with bit rates in the 100 kbps1 mbps range , 
 video at about 30 fps , spatial resolution at about 360  240 pixels ) that specifically 
 aims to develop algorithms to achieve diagnostic quality .
a few studies [ 1419 ] have 
 specifically addressed the quality of medical images or video in telemedicine 
 systems .
martini and mazzotti   address the design of a video compression and transmission 
 system for medical applications based on metrics such as 
 peak - signal - to - noise - ratio ( psnr ) .
psnr , however , is a mathematical formula to 
 measure the amount of compression in a video and has no definite relationship 
 to diagnostic quality .  
ashraf and akbar 
  demonstrate diagnostically lossless ( dl ) medical images by using lossless 
 coding within the roi , but this can not be extended to video because of bit - rate 
 limitations .
implement roi compression on angiogram video 
 sequences and verify that their results are of dl quality .
however , this 
 outcome is incidental and not actively pursued as part of the algorithm design . 
 in summary ,
the authors are unaware of any work in literature within the 
 context under consideration ( wireless networks with bit rates in the 100 kbps1 mbps range , 
 video at about 30 fps , spatial resolution at about 360  240 pixels ) that specifically 
 aims to develop algorithms to achieve diagnostic quality .
the goal of our elastic roi coding 
 algorithm is to enable mobile medical assessment .
this is achieved by ( 1 ) incorporating 
 physician expert feedback into the design of the video coding system in order 
 to assess the bit rate at which dl video quality is achieved ; ( 2 ) making the 
 system operate with dl video quality at bit rates available in wireless 
 networks using roi - based bit allocation .
roi definition ( i.e. , segmentation 
 and tracking ) is also an important problem in roi system design that has been 
 widely researched .
it involves a mounted 
 camera in an emergency unit with standard lighting where the patient is located . 

thus , for the purposes of our research , we focus on the more pressing issue of achieving dl 
 video quality given a priori manual 
 selection of the roi by the physician experts .
mathematical 
 losslessness ( ml ) : the compression is lossless and therefore produces no loss 
 of any digital information.diagnostic 
 losslessness ( dl ) : there is lossy compression , but it does not compromise 
 visual medical assessment in any way . in other words ,
the video quality is 
 completely sufficient , according to physician experts , for making a diagnostic 
 evaluation.best effort ( be ) : 
 this is the quality achieved with the highest possible compression in a video 
 coding scheme ( i.e. , in a limited bandwidth environment , it is the poorest 
 quality level ) . 
  mathematical 
 losslessness ( ml ) : the compression is lossless and therefore produces no loss 
 of any digital information .
diagnostic 
 losslessness ( dl ) : there is lossy compression , but it does not compromise 
 visual medical assessment in any way . in other words ,
the video quality is 
 completely sufficient , according to physician experts , for making a diagnostic 
 evaluation .
best effort ( be ) : 
 this is the quality achieved with the highest possible compression in a video 
 coding scheme ( i.e. , in a limited bandwidth environment , it is the poorest 
 quality level ) .
uncompressed color video requires 
 12 bpp , ml color video requires about 46 bpp , 
 corresponding to lossless compression schemes in literature .
prior to our work , we found no 
 literature that addresses the bit - rate requirement for dl .
in other words , it is 
 unknown how much lower the bit rate for dl level is in comparison to the ml level . 

the procedure involves a set of 
 videos compressed at a variety of bit rates and uniform spatial quality , that 
 is , uniform compression .
physician experts blind to the degree of compression in 
 the videos presented to them in random order identify maximum levels of 
 compression that can be applied while retaining dl quality .
the clinical experts 
 complete an evaluation whose format is shown in table 1 , illustrated as 
 an example specific to pediatric respiratory distress . in table 1 , each sample 
 number represents a video at a particular bit rate .
the medical expert lists 
 whichever features s / he can identify in the video . in the dl column ,
the four 
 options relate to the quality of the video for clinical assessment of each 
 feature set : 1-no , 2-maybe not , 3-likely , 4-yes .
the lowest bit rate ( tbr ) at 
 which the evaluation is 4 represents the threshold for dl for the particular feature . the sample partial table ( table 1 ) 
 represents one video evaluated by one medical expert .
we also included a 
 comments column ( not shown in table 1 ) on the experts scoring sheets where they 
 could give comprehensive qualitative feedback on the entire video content .
the 
 purpose of this column was to seek possible interactions among different 
 features that might affect expert assessment of diagnostic quality . in our 
 current algorithm development , we did not use the information from the comments 
 section because it was generally consistent with the quantitative feedback 
 provided by the experts .
our experts insisted on evaluating 
 videos on a feature - by - feature basis , and not simply the video as a whole 
 because , they explained , during in person evaluation they normally control the 
 order of their inspection , often alternating between the inspection of an 
 individual feature or finding and a general patient assessment .
further , they 
 emphasized that in urgent situations , they would need   the ability to control the order of 
 inspection without the delays and potential for errors involved in 
 communicating telephonic instructions to a remote and potentially clinically 
 inexperienced camera holder .
the design of the system was therefore developed with 
 recognition of this requirement , and with the following logic .
for example , a 
 segment containing comprehensive frontal view of an infant possesses a wealth 
 of information in terms of medical features , whereas a segment depicting the 
 rear view of the infant head would be expected to contain more limited 
 information .
if two such video segments coded at several different bit rates 
 are evaluated by clinical experts , evaluation of the overall video would still 
 lead to limited information unless the clinician had the ability to examine 
 closely the specific features visible in the over - all videos .
this sequence is 
 in fact how clinical evaluation is performed when the patient and the clinician 
 are together in person .
if tbr_dli represents the tbr 
 required for dl for feature i averaged over all the physician experts , then the 
 corresponding bpp value , denoted by bpp_dli is ( 1)bpp_dli = tbr_dlifr_ratefr_size , where fr_rate represents the frame 
 rate ( in fps ) and fr_size represents the spatial resolution of the videos 
 ( assuming one fixed value ) . to obtain one bpp value for dl encompassing all 
 features , denoted by bpp_dl ,
this is a conservative approach that ensures that dl quality is 
 achieved under all possible scenarios  visual 
 information being encoded has lots of features , or few features . in this work ,
we focus upon the 
 problem of acute childhood respiratory distress as a representative example of 
 an urgent need for remote consultation . with parental informed consent ( mcg 
 human assurance committee
06 - 04 - 284 ) , we collected 11 videos of pediatric 
 patients in respiratory distress and used physician experts to complete 
 evaluation templates ( table 1 ) .
each video has a spatial resolution 
 of 360  240 pixels , a frame rate of 30 fps , and standard hospital emergency room lighting 
 conditions .
the videos were encoded at uniform 
 spatial quality using a standard mpeg-2 reference implementation at 3 bit rates500 kbps , 
 1000 kbps , and 1500 kbps .
for simplicity 
 of presentation here , the video sample names have been converted to the form 
 videoname_tbr .
however , as stated previously , clinician 
 evaluators were blind to the compression and videos were presented in random 
 order .
five videos were used to measure the 
 bit rate at which dl quality is achieved .
the remaining 6 videos were used for 
 testing the efficacy of the proposed algorithm .
video er08 , although evaluated 
 and displayed here , was left for testing purposes . after averaging over the 5 
 videos and all the experts , the required tbr values for dl for individual 
 features
we also 
 observe the clustering of tbr values for dl for different medical features , 
 irrespective of the patient race .
choosing the maximum tbr value results in dl 
 over all features , and this value is 1077 kbps , that is , 0.42 bpp corresponding 
 to the rr feature . to put roi compression in perspective 
 with the above determined bpp value ,
figure 1 illustrates the bit per pixel 
 density at a tbr of 500 kbps with roi compression and uniform compression of a 
 frame of size 360  240 pixels .
the roi is assumed to be 25% of the total spatial 
 size of the frame .
thus , with roi compression , dl is 
 achievable in the roi even at 500 kbps , whereas it is not achievable with 
 uniform compression .
this is because the bit rate in the roi at 500 kbps is 270 kbps , 
 which is equivalent to an overall bit rate of 270  4 = 1080 kbps , which 
 corresponds to the dl bit rate .
the   encoder must use the information 
 on measurement of bit rate at which dl quality is achieved and map ml , dl , and 
 be quality levels to the roi and bkgrnd regions in the video .
this is done 
 using the notion of an encoder state which is defined by a pair of quality 
 levels , one corresponding to the roi and the other corresponding to the bkgrnd . 

the complete state table for the encoder is shown in table 4 . in the table ,
the states are numbered from 1 to 6 according to 
 decreasing priority , based on the basic premise that the roi quality should be 
 at least the same as or better than the bkgrnd quality .
also , some of the entries in 
 the state table are marked n / a , that is , not applicable because they violate 
 the basic premise that roi quality must match or exceed bkgrnd quality .
the   encoder state of operation is 
 determined through a bit - allocation algorithm between the roi and bkgrnd .
the 
 encoder state is determined or updated over every group of video frames 
 ( pictures ) , referred to as a gop . typically , there are 15 frames in a gop . 

state determination is done at the gop level in order to average over its 
 constituent frames which typically have different levels of compression .
for 
 the video currently being encoded , the nominal number of bits required per gop 
 for dl in the roi denoted by r_roi_dl is ( 2)r_roi_dl = bpp_dlroi_sizen_gop , where roi_size is the average size of 
 the roi for the current gop and may be a window of standard shape and size 
 around the feature(s ) .
similar 
 expressions hold for the nominal number of bits required for other quality 
 levels for the roi and bkgrnd .
for ml and be quality levels , bpp_ml and bpp_be 
 values documented in literature are used . at the beginning of a new gop
, the 
 encoder tries to occupy the highest priority state given the current tbr . if r 
 denotes the target number of bits in a gop ( which is directly proportional to 
 the tbr ) , the encoder checks if it can occupy state 1 , governed by the 
 condition : r > r_roi_ml + r_bkgrnd_ml .
if so , it occupies state 1 , otherwise 
 it checks if it can occupy state 2 , governed by the condition : r > r_roi_ml 
 + r_bkgrnd_dl .
this procedure continues until it occupies one of the six 
 possible states , and is illustrated in figure 2 .  

figure 3 is analogous to figure 1 and 
 depicts the bit per pixel density in the roi and bkgrnd in each state . in each 
 state , the bkgrnd is allocated bits first , and then the remaining bits are 
 allocated to the roi .
this is done to ensure that the roi gets bits in excess 
 of the nominal value allocated to the roi in that state .
for example , in state 
 5 , where the roi has dl quality and bkgrnd has be quality , the allocation is 
 done as ( 3)r_bkgrnd = r_bkgrnd_be , r_roi = rr_bkgrnd , where r_roi and r_bkgrnd denote the 
 allocated number of bits in a gop for the roi and bkgrnd , respectively . if the 
 encoder determines that state 5 is the current gop state , the bkgrnd gets 
 allocated r_bkgrnd_be bits , and the roi gets allocated at least r_roi_dl bits . in state 6 ,
the tbr is low , so all the bits 
 are allocated to the roi , but we still do not achieve dl quality . at a given tbr , the encoder occupies 
 a particular state , which is the highest priority under the given conditions . 

when the tbr changes , as in a variable bit - rate ( vbr ) wireless channel , the 
 encoder may transition to a new state at the beginning of a new gop . depending 
 on the change in tbr , the encoder may move to a higher priority state or a 
 lower priority state .
the encoder operation is based on bit 
 allocations to roi and bkgrnd based on the nominal bpp_dl value obtained in section 2.1 above .
the encoder is said to have dl quality in the roi if its current state 
 corresponds to a dl quality for the roi , that is , states 4 and 5 .
the bit 
 allocations in these states are ( 4)state  4 :  r_bkgrnd = r_bkgrnd_dl ,  r_roi = rr_bkgrnd , state  5 :  r_bkgrnd = r_bkgrnd_be ,  r_roi = rr_bkgrnd . for a given value of r , r_roi is 
 higher in state 5 than in state 4 because r_bkgrnd_dl is larger than 
 r_bkgrnd_be . to account for any possible deviation from nominal dl quality 
 behavior ,
more bits are allocated to the roi whenever possible and deemed 
 necessary . if the encoder state is 4 , then roi bits are increased by 
 transitioning to state 5 . in state 5 ,
the strategy used to determine when the above 
 transition is necessary is based on the roi psnr value .
this value is compared 
 to the average psnr obtained at the dl bit rate for the features in section 2.1 . in states 4 and 5 , whenever the roi 
 psnr falls below this psnr threshold , a state transition flag is set to 1 . 

then , in the following gop , if the encoder state is determined ( based on the 
 approach illustrated in figure 2 ) to be either 1 , 2 , 3 , or 6 , the flag is 
 discarded because the wireless channel has determined a new quality level ( ml 
 or be ) for the roi .
if the current state is determined to be 4 or 5 and the 
 previous state was 4 , the new state is 5 .
if the previous state was 5 and the current 
 state is determined to be 4 , the new state is set to 5 .
if the previous state 
 was 5 and the current state is determined to be 5 , the new state is also 5 but 
 r_bkgrnd is lowered to r_bkgrnd_be_min .
in this section of our work , we 
 compress the 6 test videos ( not used in section 2.1 ) using the proposed elastic 
 bit - allocation algorithms implemented within an mpeg-2 encoder .
the bpp values 
 for dl and be were used to do roi encoding of the test videos at three 
 different tbr500 kbps , 750 kbps , and 1000 kbps .
these 
 bit rates correspond to the wireless sweet spot , that is , representative bit rates 
 in state - of - the - art wireless technologies .
the roi was specified by the medical 
 experts depending on the video scene , and its size typically varied from 2550% of the total 
 spatial resolution .
the evaluation template provided to the experts for the 
 test videos is identical to that presented in table 1 , rendering our 
 roi coding process transparent to the expert . 

table 6 shows the 
 completed evaluation template by a medical expert for video er08 .
a comparison 
 with table 2 reveals improvement in performance at 
 500 kbps over the uniform compression case .
this is true since the corresponding 
 values in table 7 are populated with 3 seconds and 4 seconds indicative of dl 
 quality whereas the values in table 2 are populated with 2 seconds indicative 
 of non - dl quality . 

figure 5 shows a frame representative 
 of overall quality of video er08 at 500 kbps with elastic roi - based bit 
 allocation .
this figure may be compared to figure 4 to compare with the same 
 frame when uncompressed ( figure 4(a ) ) and when uniformly compressed at 500 kbps 
 ( figure 4(b ) ) .
it may be observed that the bkgrnd bit rate is lower in figure 5 
 compared to figure 4(b ) .
however , visually , the artifacts are prominent to 
 about the same degree in both cases .
this is because artifacts do not get too 
 much worse when the bit rate is small .
however , the key is the achievement of a 
 high level of quality in the roi . to gain an understanding of the 
 general performance of our method ,
these were then averaged to obtain a mean (  ) and standard 
 deviation (  ) score for dl at 
 500 kbps , 750 kbps , and 1000 kbps .
is expected within the roi ( unless the encoder is in state 6 ) 
 irrespective of the particular medical feature , the features column does not 
 appear in the table . at 500 kbps ,
750 kbps , and 1000 kbps ,  dl values are 3.67 , 
 3.78 , and 3.79 , respectively .
the standard deviation values are indicative of 
 the variability about the mean values . with uniform compression at 500 kbps ,  

dl value is 2.29 , with a standard deviation  of 0.57 . at 750 kbps ,
thus , with the proposed 
 method , at 500 kbps the diagnostic quality of the video is 92% , compared to 57% 
 with uniform compression .
our work targets , and is relevant to , 
 real - time remote diagnosis of emergency medical events using wirelessly 
 transmitted videos of patients .
this scenario is driven by the following : ( a ) 
 the need for urgent evaluation of a distant patient that can not be moved to the 
 expert , or at least can not be moved to the expert location in time to make a 
 required critical decision , ( b ) the inadequacy of a still image to convey the 
 clinical information necessary for decision - making , and ( c ) cost and 
 infrastructure issues that preclude high - bit rate transmission of the captured video . 

we describe the design of an elastic video coding system , based on roi , to 
 achieve diagnostically lossless visual communications over wireless 
 telemedicine networks .
a novel algorithm is proposed that was developed using 
 physician expert feedback to measure the bit rate at which dl quality is 
 achieved . this bit rate
is then incorporated into the encoder design which is 
 based on operation in an optimal state at a particular tbr .
the bit rate 
 required for dl quality was determined to be 0.42 bpp for mpeg-2 video 
 compression and 360  240 pixel resolution .
it was validated by physician expert 
 evaluation , and roi encoded videos were deemed to be diagnostically lossless at 
 500 kbps , as compared to 1077 kbps for uniformly encoded videos .
roi encoding 
 with our proposed algorithms results in dl video quality over the bit rate range 
 corresponding to state - of - the - art wireless technologies .
 in real - time remote diagnosis of emergency medical events , mobility can be enabled by wireless video communications . 
 however , clinical use of this potential advance will depend on definitive and compelling demonstrations of the reliability of diagnostic quality video . because the medical domain has its own fidelity criteria 
 , it is important to incorporate diagnostic video quality criteria into any video compression system design . to this end 
 , we used flexible algorithms for region - of - interest ( roi ) video compression and obtained feedback from medical experts to develop criteria for diagnostically lossless ( dl ) quality . 
 the design of the system occurred in three steps - measurement of bit rate at which dl quality is achieved through evaluation of videos by medical experts , incorporation of that information into a flexible video encoder through the notion of encoder states , and an encoder state update option based on a built - in quality criterion . 
 medical experts then evaluated our system for the diagnostic quality of the video , allowing us to verify that it is possible to realize dl quality in the roi at practical communication data transfer rates , enabling mobile medical assessment over bit - rate limited wireless channels . 
 this work lays the scientific foundation for additional validation through prototyped technology , field testing , and clinical trials . 
1. Introduction
2. Elastic ROI Coding System for  Diagnostically Lossless Quality
3. Evaluation of the
Diagnostic Quality Encoding System
4. Conclusion