Patent Publication Number: US-8983223-B2

Title: Low-complexity bilateral filter (BF) implementation in a data processing device

Description:
FIELD OF TECHNOLOGY 
     This disclosure relates generally to video/image filtering and, more particularly, to a low-complexity bilateral filter (BF) implementation in a data processing device. 
     BACKGROUND 
     A data processing device such as a mobile device (e.g., a mobile phone, a tablet) may have a low processing capability associated therewith. A user of the data processing device may view video data thereon. Video frames of the video data may include edges therein. An edge may be a portion of a video frame around which there is a change in image intensity level above a threshold value. For edge preservation, the data processing device may include a bilateral filter (BF) implemented in a post-processing engine executing on a processor thereof. The BF may be represented as a product of two Gaussian functions, viz. a spatial Gaussian function and a range Gaussian function, divided by a normalization factor. The complexity of the BF representation may not be conducive to execution of the post-processing engine on the data processing device. Thus, the user may be able to enjoy only limited capabilities provided through the post-processing engine. 
     SUMMARY 
     Disclosed are a method, a device and/or a system of a low-complexity bilateral filter (BF) implementation in a data processing device. 
     In one aspect, a method includes implementing, through a processor communicatively coupled to a memory and/or a hardware block, a Bilateral Filter (BF) including a spatial filter component and a range filter component, and implementing, through the processor and/or the hardware block, the spatial filter component with an appropriate function having reduced complexity compared to a Gaussian function to allow for focus on the range filter component. The method also includes determining, through the processor, filter tap value(s) related to the range filter component as a function of radiometric distance between a pixel of a video frame and/or an image and other pixels thereof based on a pre-computed corpus of data related to execution of an application in accordance with a filtering requirement of the pixel by the application. 
     Further, the method includes constraining, through the processor, the filter tap value(s) to a form i×base to further reduce complexity of filtering of the pixel based on the BF implementation. i is an integer and base is a floating point base. 
     In another aspect, a data processing device includes a memory, and a processor communicatively coupled to the memory. The processor is configured to execute instructions to implement a BF including a spatial filter component and a range filter component, and instructions to implement the spatial filter component with an appropriate function having reduced complexity compared to a Gaussian function to allow for focus on the range filter component. The processor is also configured to execute instructions to determine filter tap value(s) related to the range filter component as a function of radiometric distance between a pixel of a video frame and/or an image and other pixels thereof based on a pre-computed corpus of data related to execution of an application in accordance with a filtering requirement of the pixel by the application. 
     Further, the processor is configured to execute instructions to constrain the filter tap value(s) to a form i×base to further reduce complexity of filtering of the pixel based on the BF implementation. i is an integer and base is a floating point base. 
     In yet another aspect, a system includes a server and/or a storage medium including pre-computed corpus data related to execution of an application. The system also includes a data processing device communicatively coupled to the server and/or the storage medium. The data processing device includes a processor communicatively coupled to a memory. The processor is configured to execute instructions to implement a BF including a spatial filter component and a range filter component, and instructions to implement the spatial filter component with an appropriate function having reduced complexity compared to a Gaussian function to allow for focus on the range filter component. 
     The processor is also configured to execute instructions to determine filter tap value(s) related to the range filter component as a function of radiometric distance between a pixel of a video frame and/or an image and other pixels thereof based on the pre-computed corpus of data in accordance with a filtering requirement of the pixel by the application. Further, the processor is configured to execute instructions to constrain the filter tap value(s) to a form i×base to further reduce complexity of filtering of the pixel based on the BF implementation. i is an integer and base is a floating point base. 
     The methods and systems disclosed herein may be implemented in any means for achieving various aspects, and may be executed in a form of a non-transitory machine-readable medium embodying a set of instructions that, when executed by a machine, cause the machine to perform any of the operations disclosed herein. 
     Other features will be apparent from the accompanying drawings and from the detailed description that follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments of this invention are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements and in which: 
         FIG. 1  is a schematic view of a data processing system, according to one or more embodiments. 
         FIG. 2  is a schematic view of separability of a Bilateral Filter (BF) implementation in a data processing device of the data processing system of  FIG. 1  into a horizontal filter and a vertical filter, according to one or more embodiments. 
         FIG. 3  is a schematic view of an implementation of the horizontal filter of  FIG. 2 , according to one or more embodiments. 
         FIG. 4  is a schematic view of a server of the data processing system of  FIG. 1  including a processor communicatively coupled to a memory, according to one or more embodiments. 
         FIG. 5  is a process flow diagram detailing the operations involved in a low-complexity BF implementation in the data processing device of  FIG. 1 , according to one or more embodiments. 
     
    
    
     Other features of the present embodiments will be apparent from the accompanying drawings and from the detailed description that follows. 
     DETAILED DESCRIPTION 
     Example embodiments, as described below, may be used to provide a method, a device, and/or a system of a low-complexity bilateral filter (BF) implementation in a data processing device. Although the present embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the various embodiments. 
       FIG. 1  is a schematic diagram of a data processing system  100 , according to one or more embodiments. In one or more embodiments, data processing system  100  may include one or more server(s) (e.g., server  160 ) and one or more data processing device(s) (e.g., data processing device  170 ) configured to utilize the services provided through the one or more server(s) (e.g., through a computer network  106  such as Internet, a Wide Area Network (WAN) and a Local Area Network (LAN)). Alternately, in one or more embodiments, data processing system  100  may merely include data processing device  170  and processing/optimization of data to be discussed herein may be performed locally therethrough. In one or more embodiments, data processing device  170  may be a desktop computer, a laptop computer, a notebook computer, a netbook or a mobile device such as a tablet or a mobile phone. Other forms of data processing device  170  are within the scope of the exemplary embodiments discussed herein. 
     In one or more embodiments, data processing device  170  may include a processor  102  (e.g., a Central Processing Unit (CPU), a Graphics Processing Unit (GPU)) communicatively coupled to a memory  104  (e.g., a volatile memory and/or a non-volatile memory); memory  104  may include storage locations addressable through processor  102 . In one or more embodiments, data processing device  170  and/or server  160  may be employed in one or more applications such as video enhancement, camera noise reduction, video coding artifact reduction, video abstraction and optical flow estimation. In order to cater to the filtering requirements of image/video processing associated with the aforementioned one or more embodiments, a bilateral filter (BF) may be implemented in data processing device  170 . Specifically, in the case of data processing device  170  being a mobile device, the BF may be used both during video capture and video playback. 
     Video frames processed through data processing device  170  may include edges therein. An edge may be a point of a video frame around which there is a change in image intensity level above a threshold value. As the edge defines a boundary of transition between image intensity levels, the edge may be a feature of the video frame worth preserving during filtering. Although the edge-preserving capability of the BF is extremely useful, the BF may provide for high complexity of processing due to a non-linear nature thereof. The high complexity may not be conducive to the implementation of the BF ( FIG. 1  shows a BF engine  110  to be executed through processor  102  as being stored in memory  104 ; BF engine  110  may include the requisite set of instructions to provide the functionalities associated with the BF) in a power-constrained data processing device  170  such as a mobile device. Similarly, the non-linear adaptive BF may incur silicon cost when implemented as a hardware block associated with data processing device  170 . 
     An output of an example BF implemented in data processing device  170  for the edge preservation discussed above may generically be represented as: 
                       BF   ⁡     [   I   ]       p     =       1     w   p       ⁢       ∑     q   ∈   S       ⁢           ⁢         G     σ   s       ⁡     (          p   -   q          )       ⁢       G     σ   r       ⁡     (            I   p     -     I   q            )       ⁢     I   q                   (   1   )               
In words, for an input video frame  122   1-N  I of video data  116  (exemplary embodiments discussed herein are also applicable to image data) shown as being stored in memory  104 , the output BF[I] p  for a pixel p may be expressed as example Equation (1). Here, σ s  and σ r  are the spatial deviation and the radiometric deviation respectively; G σ     s    is a Gaussian function associated with spatial pixel location (spatial filter portion) and G σ     r    is a Gaussian function associated with pixel intensity (range filter portion); and the normalization factor W p  is given by:
 
     
       
         
           
             
               
                 
                   
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     The normalization factor may ensure that filter weights for all pixels add up to 1. Thus, the weights of the BF may depend on both the spatial Gaussian function and the range Gaussian function. The BF may be adapted by varying σ s  and/or σ r . The BF is known to one skilled in the art; therefore, detailed discussion of concepts associated therewith has been skipped for the sake of brevity and clarity. 
     The Gaussian exponential functions in example Equations (1) and (2) may contribute to computational complexity of the BF. Although Gaussian functions discussed above may predominantly be used to represent the spatial filter portion and the range filter portion of the BF, the general representation of the BF allows for a more expanded family of functions to be utilized. Exemplary embodiments exploit the possibility of a reduction in complexity of the BF implementation, as will be discussed below. 
     The basic BF implementation may be separated into the spatial filter portion, the range filter portion and a normalization portion, as seen in example Equation (1). To generalize the formulation of the BF, example Equations (1) and (2) may be rewritten as example Equations (3) and (4) 
     
       
         
           
             
               
                 
                   
                     
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     Here, F σ     s    is a general function (e.g., Gaussian, non-Gaussian) associated with spatial pixel location and G σ     r    is a general function (e.g., Gaussian, non-Gaussian) associated with pixel intensity (range). 
     In one or more embodiments, the generalized formulation of the BF as including a spatial filter portion and a radiometric/range filter portion may render the BF amenable to a separable implementation, at least with respect to the spatial filter portion, to increase computational speed of the filtering. In other words, in one or more embodiments, the spatial filter portion of the BF may be applied across one spatial dimension (e.g., say, x-axis) and the intermediate output may then be filtered in other spatial dimension(s) (e.g., in the case of a two-dimensional (2D) image, the other dimension is a dimensional perpendicular to the x-axis, viz. y-axis).  FIG. 2  shows the separability of the BF implementation into a horizontal filter  202  (e.g., x-axis) and a vertical filter  204 , according to one or more embodiments. 
     In addition, in one or more embodiments, the envelope of the spatial filter portion may be simplified into a rectangular function to allow for the focus to be solely on the radiometric/range filter portion. In one or more embodiments, even in the case of the range filter portion, a Lookup table (LUT) (e.g., LUT  124  stored in memory  104  or implemented in a hardware block associated with data processing device  170 ) may be utilized to pre-compute and store function values for various radiometric distances. Thus, in the simplest form, the spatial filter portion corresponding to horizontal filter  202  may be a 1×N rectangular filter (N being a positive integer). It should be noted that non-Gaussian non-complex functions other than the simple rectangular function may be utilized instead. For example, in certain applications of the BF, a series of rectangular functions may be utilized in the spatial filter portion. 
     The LUT  124  based approach discussed above may be based on picking a pre-calculated filter tap value from LUT  124  for a corresponding radiometric difference |I p −I q |; here, pixels close in intensity levels to a current pixel may be associated with higher weights/filter tap values, thereby contributing to reduction in noise due to diffusion of edges. In one or more embodiments, the filter tap values may be static due to computation thereof in advance. In an example case of an 8-bit video frame  122   1-N , entries of LUT  124  may vary from 0 to 255. For each value obtained, the filter output may have to be calculated and divided by the normalization factor. Exemplary embodiments provide for a means to dispense with the aforementioned division, as will be discussed below. 
     In one or more embodiments, following the accomplishment of the range filter operation through LUT  124  and the normalization operation, a filtered output of the aforementioned set of operations may be applied to a vertical filter  204  to produce a final output (e.g., output  206 ) as the filtered video frame. In the abovementioned example of the spatial filter portion being initiated through a 1×N rectangular filter, vertical filter  204  may be an appropriate N×1 rectangular filter. 
     In one or more embodiments, the range filter portion may also be simplified through a novel technique to compute candidate tap values thereof. Although the filter tap values can be computed through simplifying an exponential function to various extents, exemplary embodiments provide for a novel technique to utilize application specific data for computing cluster(s) that best describe the prospective candidate tap values of the range filter portion.  FIG. 1  shows an application  188  executing on data processing device  170 . In one or more embodiments, data processing device  170  may have a history of execution of application  188  associated therewith. In one or more embodiments, said history of execution may generate enough data suitable for clustering. Portions of the generated data may have also been run through exponential (and/or non-exponential) forms of the function implementation representing the range filter portion. In one or more embodiments, by clustering the aforementioned data to choose the best suited candidate filter tap values, the complexity of the normalization portion of the BF implementation may also be reduced. 
     In one or more embodiments, the centroids of the abovementioned clusters may be the potential candidate filter tap values. Further, in one or more embodiments, through restricting the aforementioned centroids and, hence, the potential candidate filter tap values to a form i×base (where i is an integer and base is a floating point base), the normalization operation may be restricted to a simple integer division (the floating point base portion may be dispensed with due to presence thereof in a numerator and a denominator of the BF output equation). In one or more embodiments, steering the centroids of the clusters toward specific filter tap values may be put forth as an optimization problem subject to a set of constraints as: 
     
       
         
           
             
               
                 
                   
                     
                       
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     In example optimization (5), C is the set of centroids found through clustering, Ĉ is the new filter realized from the clustering that is representable as an integer multiple of the floating point base, {i}×base. Example optimization (5) may solve for a set of {i}×base for which ∥C−Ĉ({i}×base)∥ p  is at a minimum value thereof. p is a suitable index to which ∥C−Ĉ({i}×base)∥ is raised. If required, further constraints may be imposed on the set of integers {i}; for example, the candidate filter tap values may be made to be bit-shift friendly (to be discussed below). It should be noted that the clustering operation and/or the steering of the centroids may be performed as pre-processing operation(s); the aforementioned pre-processing operation(s) may not affect the low-complexity filtering implemented through BF engine  110  executing on data processing device  170 . 
     In one or more embodiments, restriction of a length of the potential candidate tap values to an appropriate number may also restrict a size of an inverse lookup table (e.g., ILUT  126  stored in memory  104 ; ILUT  126  is to account for the normalization operation). Thus, in one or more embodiments, an ultra-low complex BF filter may be realized through BF engine  110 . 
     In one or more embodiments, as mentioned above, the candidate filter tap values may be steered to be amenable to bit-shifting to further reduce the complexity of the BF implementation. The reduction in complexity may include dispensing with computationally expensive operations such as multiplication and division. In the case of data processing device  170  including a low-end processor  102 , the aforementioned reduction in complexity may be critical. It should be noted that the functions discussed herein with respect to the range filter portion and/or the spatial filter portion are mere examples; exemplary embodiments provide for flexibility in the choices therein; all low-complexity candidate BFs that maintain the edge-preserving nature of the traditional BF are within the scope of the exemplary embodiments discussed herein. 
     In one or more embodiments, to optimize the amount of space consumed by ILUT  126 , bit locations associated with entries thereof may be fully utilized. In one or more embodiments, each inverse value of an integer i may be stored in ILUT  126  in the form (2 k /i also being an integer). In one or more embodiments, k may be chosen such that as many significant bits of the inverse value may be stored in ILUT  126 . In one or more embodiments, 2 k  may then be bit-shifted later. In one or more embodiments, companding may be implemented to store both k and a set of 2 k  and i (and/or 2 k /i) in ILUT  126 . For example, when ILUT  126  includes 8-bit entries, the first 5 bits of each entry may be related to the 5 significant bits of the mantis sa/significand; the last 3 bits may be related to the value of k, and may indicate the number of bit-shifts required. It should be noted that the choice of the number of bits is not limited to 8. Depending on the precision requirements of application  188 , the number of bits may exceed 8 or be less than 8. 
     In an example scenario, the intensity level for a current pixel may be 150 and the intensity level for a preceding pixel  100 . The |I p −I q | of 50 may be looked up in LUT  124  and the appropriate filter tap value(s) selected. As discussed above, the filter tap value(s) may be chosen through the clustering process. The clustering process may also restrict a number of appropriate filter tap value(s) to a small number that further reduces the size of LUT  124 ; search space in LUT  124  may also be reduced. Further, if the constraints deem that the number of appropriate filter tap value(s) is, say, 11 and that the floating point base (base) is 1.25, the aforementioned filter tap value(s) may be 0, 1.25, 2.5 and so on. Wherever filtering is done in video frame  122   1-N , the associated filter tap value(s) may only be combination(s) of the aforementioned values. In the case of looking up a number with an infinitely repeating decimal through ILUT  126 , a maximum number of significant bits of the repeating decimal may be incorporated in an entry of ILUT  126  to ensure that the filter computation is as accurate as possible. 
     Exemplary embodiments may also be explained in terms of catering to a particular application  188 . Consider a video capturing application on a mobile phone (example data processing device  170 ) where sensor noise in low light conditions is a major issue impacting quality. Here, exemplary embodiments allow for computation of optimal yet low-complexity filters by clustering data generated at various lighting conditions. Thus, a small set of candidate range filter tap values may be generated, which directly results in extremely fast lookup operation(s) from ILUT  126  and, thereby, computation of the final filter values. The low complexity involved herein enables the BF to be used in power-constrained applications. 
       FIG. 3  summarizes the implementation of horizontal filter  202 , according to one or more embodiments. In one or more embodiments, horizontal filter  202  may have a corpus of input data  302  that is utilized during the clustering discussed above; the clustering may be a pre-computing process. In one or more embodiments, for a pixel of an input video frame  122   1-N , LUT  124  may be searched (e.g., through the execution of BF engine  110  on processor  102 ) utilizing radiometric distances between the pixel and other pixels as an index to determine the potential candidate filter tap values. As discussed above, in one or more embodiments, clustering engine  304  may enable the choice of the best candidate filter tap values (e.g., candidate filter tap values  306 ) from LUT  124 . In one or more embodiments, candidate filter tap values  306  may be fed to a range filter engine  308  that executes the range filter portion operations associated with example Equation (3). Further, in one or more embodiments, candidate filter tap values  306  may be fed to a normalization engine  310  that executes the normalization operations associated with example Equation (3). 
     In one or more embodiments, normalization engine  310  may leverage ILUT  126  that includes entries stored in the form 2 k /i. In one or more embodiments, data related to the most significant bits (MSB) of the entry may be combined with the output of range filter engine  308 ; the subsequent output may be bit-shifted (e.g., through bit-shifting engine  312 ) as indicated by the least significant bits (LSB) of the entry. Said bit-shifting may account for dispensing with the division by the normalization factor in example Equation (3). In one or more embodiments, the final output (e.g., output  314 ) of horizontal filter  202  may then be filtered through vertical filter  204  to arrive at output  206 . 
     It should be noted that LUT  124  and ILUT  126  may be implemented in a same table; further, LUT  124  and/or ILUT  126  may be implemented in hardware and/or software. Also, lookup tables are merely shown for the sake of example purposes. Alternate implementations such as executing fetching operations of “entries” directly from memory  104  are within the scope of the exemplary embodiments discussed herein. 
     Clustering engine  304  may execute on server  160 ; alternately, clustering engine  304  may execute on data processing device  170 . In one example embodiment, server  160  may provide the clustering as a service to data processing device  170 . An example implementation of clustering through clustering engine  304  may involve k-means clustering; other forms of clustering may also be implemented therein. All reasonable modifications, implementations and/or applications are within the scope of the exemplary embodiments discussed herein. 
     It should be noted that while the clustering discussed herein is effectively utilized to steer the centroid(s) to filter tap value(s), other ways of determining the filter tap value(s) of the form i×base based on corpus of input data  302  are within the scope of the exemplary embodiments discussed herein. Further, while BF engine  110 , LUT  124  and ILUT  126  are preferentially shown as part of data processing device  170  to address latency-critical applications, BF engine  110 , LUT  124  and/or ILUT  126  may also be part of server  160 . In the case of the computing platform of server  160  being virtualized to serve multiple client devices (e.g., data processing device  170 ), the low-complexity execution of BF engine  110  on server  160  may enable improved quality of service therefrom. Also, the aforementioned modification dispenses with issues related to server  160  being a low-end device. 
       FIG. 4  shows server  160  including a processor  402  communicatively coupled to a memory  404 , in accordance with one or more alternate embodiments. Here, memory  404  may include BF engine  110  to be executed on processor  402 , LUT  124  and/or ILUT  126 . Data processing device  170  is shown as being communicatively coupled to server  160  through computer network  106  (e.g., Internet, a WAN, a LAN). In one or more embodiments, data processing device  170  may provide video data  116  to be filtered through server  160 .  FIG. 4  also shows clustering engine  304  and corpus of input data  302  as being stored in memory  404 . 
     Last but not the least, the results of clustering engine  304  may be transmitted from server  160  to data processing device  170  to be utilized through BF engine  110  (see the embodiment of  FIG. 1 ). Alternately, the results may be stored in memory  104  and/or made available on a non-transitory medium (e.g., storage medium) readable through data processing device  170  and/or server  160  such as a Compact Disc (CD), a Digital Video Disc (DVD), and a hard drive. Variations therein are within the scope of the exemplary embodiments. 
     It should be noted that utilization of fixed-point arithmetic may enable realization of a BF and all operations associated therewith in integer arithmetic; however, such an implementation may be computation/area expensive, whether in software or hardware. For example, a processor based on ARM architecture may consume several cycles for a division operation. Exemplary embodiments render the integer division redundant by: 
     a) effectively reducing the number of filter tap values through clustering; this reduces a size of LUT  124  by providing for a small number of possible combinations of the filter tap values, and 
     b) providing for filter tap values with low bit-width; this provides for a small ILUT  126 ; the division operation may be reversed by bit-shifting discussed above. 
     In one or more embodiments, a combination of a) and b) may enable achieving ultra-low complexity normalization without sacrificing numerical precision. 
       FIG. 5  shows a process flow diagram detailing the operations involved in realizing a low-complexity BF, according to one or more embodiments. In one or more embodiments, operation  502  may involve implementing, through processor  102  and/or a hardware block, a BF including a spatial filter component and a range filter component. In one or more embodiments, operation  504  may involve implementing, through processor  102  and/or the hardware block, the spatial filter component with an appropriate function having reduced complexity compared to a Gaussian function to allow for focus on the range filter component. 
     In one or more embodiments, operation  506  may involve determining, through processor  102 , filter tap value(s) related to the range filter component as a function of radiometric distance between a pixel of video frame  122   1-N  and/or an image and other pixels thereof based on a pre-computed corpus of data (e.g., pre-computed corpus of input data  302 ) related to execution of application  188  in accordance with a filtering requirement of the pixel by application  188 . In one or more embodiments, operation  508  may then involve constraining, through processor  102 , the filter tap value(s) to a form i×base to further reduce complexity of filtering of the pixel based on the BF implementation. In one or more embodiments, i may be an integer and base may be a floating point base. 
     Although the present embodiments have been described with reference to specific example embodiments, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader spirit and scope of the various embodiments. For example, the various devices and modules described herein may be enabled and operated using hardware circuitry (e.g., CMOS based logic circuitry), firmware, software or any combination of hardware, firmware, and software (e.g., embodied in a non-transitory machine-readable medium). For example, the various electrical structures and methods may be embodied using transistors, logic gates, and electrical circuits (e.g., application specific integrated (ASIC) circuitry and/or Digital Signal Processor (DSP) circuitry). 
     In addition, it will be appreciated that the various operations, processes, and methods disclosed herein may be embodied in a machine-readable medium and/or a machine accessible medium compatible with a data processing system (e.g., data processing device  170 ). Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense.