the disclosure generally pertains to video that have some scenes with varying lighting conditions . the general approach can begin with first detecting the starting and ending time of the scenes in a video . thereafter , the approach involves calculating the parameters for every frame of the scene having varying lighting conditions by interpolating the parameter values of the previous and next scenes which have static illumination . a general system diagram of the disclosed invention is illustrated as in fig1 . overall , the scheme of parameter interpolation can be carried out by first obtaining hdr video 101 having more than one scene . a next step can involve analyzing and partitioning the video 102 into scenes or segments . the analysis here involves classifying the segments into static or transient segments . this is followed by estimating parameter sets for the static segments 103 . parameter sets for transient segments are then interpolated 104 and evaluated 105 . the evaluation can be performed using an appropriate user interface . if the application of the parameter sets for the static and transient segments is satisfactory , then toning mapping is performed 106 according to the parameter sets . if the application of the parameter sets is not satisfactory , then the steps 103 , 104 and 105 are rerun such that a new parameter set for static segments is estimated and found . fig2 is a flowchart for video sequence partitioning in step 102 according to the invention . the video 101 is obtained and analyzed and partitioned 102 into static and transient segments . the process flow is as follows : the average luminance of each frame in calculated 202 ; frame - to - frame low - pass filtering 203 is applied to the average luminance data ; gradients of the low - pass filter luminance data are calculated 204 ; frame - to - frame low - pass filtering 205 is applied to the gradient data ; peaks are detected and associated bandwidths are calculated 206 from the filtered gradient data ; and the positions of the transient and static segments calculated 207 from the peak and bandwidth data . with respect to the process flow in fig2 , the frame - level average luminance of the input hdr video is analyzed . it is important to keep in mind that the average luminance of each frame can change over time . this is illustrated in fig3 which shows how the average luminance in different frames in a video sequence can change . furthermore , a video can be partitioned into several segments according to the average luminance change . successive frames having similar average luminance are characterize here as a static segment . the frames between two static segments may have gradually changing average luminance over time , which can be characterized as a transient segment . for example in fig3 , frames 0 through around 60 and frames around frame 90 at the peak having similar average luminance could be static segments and frames therebetween could comprise a transient segment . to detect the starting and ending time points of each transient segment , a scheme based on the analysis of average luminance sequence is proposed . the scheme is as follows : a ) apply a low - pass filter to the average luminance sequence 203 in fig3 to smooth luminance as illustrated in fig4 ; b ) calculate the gradients 204 of the smoothed luminance sequence as shown in fig5 and optionally apply a low - pass filter 205 to the gradients in fig5 to obtain smooth gradient as shown in fig6 ; c ) detect the peaks and bandwidth 206 of each peak in gradient curve in fig5 or the low pass filter gradient curve in fig6 ; and the starting time point of each transient area can be calculated or estimated by subtracting half of the bandwidth from the peak frame number . the ending time point of the transient area can be calculate by adding half of the bandwidth to the peak frame number . after the static and transient segments are located , the parameter set for a key frame of the static segments are estimated and then applied to the whole segment . the parameter set can include anchor points from which the image can be segmented into several regions , exposure value for each region , contrast boost parameters , and any other needed parameter for hdr tone mapping . regarding the parameter set , the exposure values , anchor points and contrast boost can be obtained by any of a number of techniques ; however , contrast boost and anchor points are discussed in greater detail below . in at least one implementation one can define a series of anchor points a i ( i = 1 . . . n ) such that each a i can be used to define a region and also generate a single exposure image . the anchor points can be chosen as : a i = l avg / e a n + 1 = 4 · a n =( n = 2 . . . n − 1 ) where e in the above equation is a constant and can take the value of 8 . the number of regions n in the above equation can be computed as below , which is able to cover the entire luminance range . l is the luminance image , which can be computed from the hdr data . if the hdr data is in xyz color space , the y component can be used as luminance image . if the hdr data uses the same primary colors as rec . 709 , the conversion from rgb color space can be done as follows : other conversions between rgb ( or other color spaces ) and the luminance image can be used depending on the format of the input picture . once the anchor point of each region is known , one can compute the weight of each pixel for each region . in general , for each region defined by the corresponding anchor point a i , the pixel with the closest value to the single exposure image is 0 . 5 and that pixel has a larger weight for that region than other pixels . thus , the weight of pixel at location ( i , j ) for region n ( defined by anchor point a n ) can be computed as below : the above computed weights take values in the range [ 0 , 1 ] and hence define a fuzzy segmentation of the luminance image into n regions . this means each region might contain all the pixels in the image , although only a portion of them might have large weights . with linear scaling , one can specify one anchor point . a pixel will be saturated and mapped to one if the luminance of that pixel exceeds the anchor point , otherwise it will be mapped to a value between 0 and 1 . therefore , the linear scaling single exposure can be defined as : where a is the anchor point and s ( x ) can be defined as : the parameter p can take values in the range [ 2 . 2 , 2 . 4 ] and represents the gamma of the output device to which the tone mapped image will be shown . the resulting image i can be quantized and displayed on a conventional ldr display . note that other definitions of s ( x ) are possible . for example , an s - shaped curve can be used instead of the power function . in general , any global mapping curve can be used for s . in another implementation , the weights are binarized , which results in either 0 or 1 and a hard segmentation : it is important to note that the anchor points a n as well as the weights w n are fixed once the segmentation is done . the contrast boost can be obtained by using a bandpass pyramid , known as a laplacian pyramid which is obtained by forming the difference between adjacent levels in a pyramid , wherein an interpolation is performed between representations at adjacent levels of resolution , to enable the computation of pixel to pixel differences . i k is one level of the laplacian pyramid of image i , where k ={ 1 , . . . , k }. k is the level and k the number of levels in the pyramid . i can represent , a color component of a digital image . i ( i , j ) represents the value of the pixel at coordinates ( i , j ). the image is then reconstructed by the pyramid i k · b k , where b is the contrast boost which is characterized as the power function here , shape controller α is in the range [ 0 , 1 ] and is typically between 0 . 2 and 0 . 4 and boost intensity β is a positive number usually in the range [ 0 , 2 ]. note that β & lt ; 1 increases the contrast , whereas β & gt ; 1 reduces the contrast . with these two parameters , one can control how much enhancement to apply to the image . it should be noted that the contrast boost pyramid b k , k ={ 1 , . . . , k } can be computed using different formulations . after the parameter sets have been determined for the static segments , an implementation of the invention uses the parameters of two adjacent static segments for interpolation . as such , the next step shown in fig1 is to interpolate the parameter set for the transient segments 104 . utilized are two interpolation schemes . one is interpolation without light adaptation . the other is interpolation with light adaptation . for the transient segments for the approach without light adaptation , one can interpolate the parameter set based on the parameters of static segments obtained from the previous step . the interpolation can be done as linear interpolation in logarithm domain , as follows . where a , b and g denote the anchor point , contrast boost level and exposure value in logarithm domain respectively ; m and n denote the starting and ending frame index of the transient segment , j is the current frame number located in the transient segment . if light adaptation is not considered , the a , b and g values are used as the parameters for transient segments . however , the human visual system performs effectively over a vast range of luminous intensities , ranging from below starlight to sunlight . the faster recovery from dark to light changes or small light decrements , which can take seconds , is known as light adaptation . in an implementation with light adaptation , the above parameter interpolation is performed to simulate how human visual system gets adapted to the light condition change . this is particularly useful when the environment becomes much brighter . essentially the idea is to introduce a delay factor in the parameter interpolation . for the first few frames in the transition period , the parameters from a previous scene are still used . after that , a linear interpolation in logarithm domain will be used : where a , b and g denote the anchor point , contrast boost level and gain value in logarithm domain respectively ; m and n denote the starting and ending frame index of the transient segment , and j is the current frame number located in the transient segment . l denotes the time delay of the start of lighting change , which simulates how human visual system gets adapted to abrupt lighting change . usually , the value of l is taken as 3 ˜ 6 frames . once the parameter sets for transient segments have been obtained that can be either directly applied to the video to tone map the video 106 , parameter sets can be evaluated 105 . as mentioned above , the evaluation can be performed using an appropriate user interface , wherein if the application of the parameter sets for the static and transient segments is satisfactory , then toning mapping is performed 106 according to the parameter sets and if the application of the parameter sets is not satisfactory , then the steps 103 , 104 and 105 are rerun such that a new parameter set for static segments is estimated and found . rerunning of the steps 103 , 104 and 105 can involve applying an additional low - pass filter operation on the gradient data and / or modifying the contrast boost parameters . while certain embodiments of the present invention have been described above , these descriptions are given for purposes of illustration and explanation . variations , changes , modifications and departures from the systems and methods disclosed above may be adopted without departure from the scope or spirit of the present invention .