Patent Publication Number: US-2013235153-A1

Title: Method and apparatus for generating depth information of an image

Description:
BACKGROUND 
     1. Field 
     One or more embodiments relate to a depth estimation apparatus and depth estimation method capable of providing depth information for converting a two dimensional (2D) monocular video into a stereoscopic video (hereinafter, ‘3D video’) when depth information is not provided, and more particularly, and a depth establishment apparatus being a 3D video conversion apparatus and a depth establishment method being a 3D video conversion method. 
     2. Description of the Related Art 
     With the dramatic increase of interest in 3D video, various research and study of 3D video has been conducted. 
     In general, it is known that a human experiences a 3D effect mostly due to the perceived binocular disparity between both eyes. Accordingly, 3D video may be realized using such a human perception. As an example, to display an object in 3D video, corresponding simultaneously displayed images are respectfully viewed by each of the right and left eyes, thereby enabling a viewer to experience the object as being in 3D. Consequently, 3D video may be realized through the manufacture of binocular video, i.e., video having separate left and right images, and displaying the binocular video. 
     SUMMARY 
     One or more embodiments provide an apparatus and system for depth measuring and/or conversion of a monocular 2D video into a 3D video without being provided depth information, with the depth measurement being accomplished through a rendering process generating depth information for the 2D video. 
     One or more embodiments may provide an apparatus for estimating depth, the apparatus including a feature information extraction unit to extract at least one feature information from an input video, the feature information being independent of any pre-established depth values of the input video, and a depth establishment unit to establish a depth of a portion of the input video based on the at least one feature information with regard to the portion of the input video. 
     One or more embodiments may provide a 3D video conversion apparatus, the apparatus including an extraction unit to extract at least one feature information from an input video, the feature information being independent of any pre-established depth values of the input video, a depth establishment unit to establish a depth of a portion of the input video based on the at least one feature information with regard to the portion of the input video, and a rendering unit to render the input video into a 3D video using the established depth of the portion of the input video. 
     One or more embodiments may provide a depth estimation method, the method including extracting at least one feature information from an input video, the feature information being independent of pre-established depth values of the input video, and establishing a depth of a portion of the input video based on the at least one feature information with regard to the portion of the input video. 
     One or more embodiments may provide a 3D video conversion method, the method including extracting at least one feature information from an input video, the feature information being independent of any pre-established depth values of the input video, establishing a depth of a portion of the input video based on the at least one feature information with regard to the portion of the input video, and rendering the input video into a 3D video using the established depth of the portion of the input video. 
     One or more embodiments may provide an apparatus for estimating depth, the apparatus including a feature information extraction unit to extract at least one feature information from an input image, the feature information being independent of any pre-established depth values of the input image, and a depth establishment unit to establish a depth of a portion of the input image based on the at least one feature information with regard to the portion of the input image. 
     One or more embodiments may provide a depth estimation method, the method including extracting at least one feature information from an input image, the feature information being independent of pre-established depth values of the input image, and establishing a depth of a portion of the input image based on the at least one feature information with regard to the portion of the input image. 
     By using a depth estimation apparatus and depth estimation method estimating a depth for converting an input 2D video into a 3D video based on at least one feature information, a 3D video conversion apparatus and 3D video conversion method with the depth estimation apparatus and the depth estimation method, a 2D video may be converted into a 3D video. 
     Additional aspects, features, and/or advantages will be set forth in part in the description which follows and, in part, will be apparent from the description, or may be learned by practice of the disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These and/or other aspects, features, and advantages will become apparent and more readily appreciated from the following description, taken in conjunction with the accompanying drawings of which: 
         FIG. 1  is a diagram illustrating a structure of a depth estimation apparatus, according to one or more embodiments; 
         FIG. 2  is a diagram illustrating a structure of a depth estimation apparatus, according to one or more other embodiments; 
         FIG. 3  is a diagram illustrating a structure of a depth estimation apparatus, according to still one or more other embodiments; 
         FIG. 4  is a diagram illustrating a structure of a depth estimation apparatus, according to yet one or more other embodiments; 
         FIG. 5  is a diagram illustrating a structure of a 3D video conversion apparatus, according to one or more embodiments; 
         FIG. 6  is a diagram illustrating a structure of a 3D video conversion apparatus, according to one or more other embodiments; 
         FIG. 7  is a diagram illustrating a structure of a 3D video conversion apparatus, according to still one or more other embodiments; 
         FIG. 8  is a diagram illustrating a structure of a 3D video conversion apparatus, according to yet one or more other embodiments; 
         FIG. 9  is flowchart illustrating a depth estimation method, according to one or more embodiments; 
         FIG. 10  is a flowchart illustrating a depth estimation method, according to one or more other embodiments; 
         FIG. 11  is a flowchart illustrating a depth estimation method, according to still one or more other embodiments; 
         FIG. 12  is a flowchart illustrating a depth estimation method, according to yet one or more other embodiments; 
         FIG. 13  is a flowchart illustrating a 3D video conversion method, according to one or more embodiments; 
         FIG. 14  is a flowchart illustrating a 3D video conversion method, according to one or more other embodiments; 
         FIG. 15  is a flowchart illustrating a 3D video conversion method, according to still one or more other embodiments; and 
         FIG. 16  is a flowchart illustrating a 3D video conversion method, according to yet one or more other embodiments. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
     Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout. In this regard, embodiments of the present invention may be embodied in many different forms and should not be construed as being limited to embodiments set forth herein. Accordingly, embodiments are merely described below, by referring to the figures, to explain aspects of the present invention. 
       FIG. 1  is a diagram illustrating a structure of a depth estimation apparatus  120 , according to one or more embodiments. Here, the depth estimation apparatus  120  may include a feature information extraction unit  121  and a depth establishment unit  122 , for example. Herein, throughout the following description, the term apparatus should be considered synonymous with elements of a physical system, not limited to a single enclosure or all described elements embodied in single respective enclosures in all embodiments, but rather, depending on embodiment, is open to being embodied together or separately in differing enclosures and/or locations through differing elements. As only another example, a respective apparatus/system or method could also be controlled through one or more processing elements/devices or implemented by a distributed network, noting that additional and alternative embodiments are equally available. 
     When an input video  110  is provided to the depth estimation apparatus  120 , the feature information extraction unit  121  may obtain at least one feature information of the input video  110 , and provide the extracted at least one feature information of the input video  110  to the depth establishment unit  122 . 
     According to one or more embodiments, the input video  110  may be a monocular video, without depth information. As another example, the input video  110  may be spatially-interleaved stereo video, temporally-interleaved stereo video, and monocular video with depth information. Also, according to one or more embodiments, the feature information extracted in the feature information extraction unit  121  may be at least one of edge information, color information, luminance information, motion information, and/or histogram information, noting that alternatives are equally available. The feature information may be with regard to an image, such as a frame of a video, or may be with regard to plural images or frames and with regard to the video. 
     The depth establishment unit  122  may establish a depth of the input video  110  based on the at least one feature information received from the feature information extraction unit  121 . 
     The depth estimation unit  120  may, thus, convert a two dimensional (2D) input video into a stereoscopic video (hereinafter, ‘3D video’) by establishing a depth of the input video  110  based on feature information of the input video  110 . If depth information for one or more pixels of the input video already exists, then the depth estimation unit  120  can convert or modify the input video to estimate depth for the one or more pixels of the input video based on the extracted feature information, with the at least one feature information being independent of such potentially preexisting or pre-established depth values of the one or more pixels. Here, the estimating of the depth for the one or more pixels may be based both on the preexisting or pre-established depth values and the extracted feature information, or solely on the extracted feature information. Alternatively, for example, if any or all preexisting or pre-established depth values for an input video do not exist, then the input video may merely be a 2D video without any previous depth information; the extracted at least one feature information would inherently be independent of any preexisting or pre-established depth values since they do not exist. In an embodiment, there may also be a review of any pre-established depth data, and if deemed accurate and precise, for example, only the pre-established depth data may be used and further estimation of depths would not be necessary. 
     According to one or more embodiments, a depth estimation apparatus  120 , such as shown in  FIG. 1 , may be implemented in alternative and/or additional embodiments. Various embodiments regarding the depth estimation apparatus  120  are described with reference to  FIGS. 2 through 4 , again noting that alternative embodiments are equally available. 
       FIG. 2  is a diagram illustrating a structure of a depth estimation apparatus  220 , according to one or more other embodiments. Here, the depth estimation apparatus  220  may include a feature information extraction unit  223  and a depth establishment unit  226 , for example. According to an embodiment, the depth establishment unit  226  may further include a depth map initialization unit  222  and a depth update unit  225 , for further example. 
     When an input video  210  is provided to the depth estimation unit  220 , the depth map initialization unit  222  may establish an initial depth of at least one pixel of the input video  210 , and store the established initial depth of the at least one pixel in a depth map  224 . The at least one pixel may be with regard to a single image, e.g., a single frame of the input video  210 , or with regard to plural images or frames of the input video  210 . For example, a depth of a portion of one frame corresponding to an identified object could be merely applied to the same object in other frames. 
     According to one or more embodiments, the input video  210  may be a monocular video. Also, according to one or more embodiments, the depth map initialization unit  222  may establish an initial depth for each frame of a sequence of the input video  210 , and store the initial depth for each frame of the sequence of the input video  210  in the depth map  224 . 
     According to an embodiment, the depth map initialization unit  222  may establish the initial depth by the below Equation 1, for example. 
         z ( x,y )= y/N   Equation 1
 
     Here, (x, y) indicates image coordinates within the input video  210 , e.g., within a frame of the input video, and z indicates a respective depth. In this instance, z may be a value between 0 to 1 depending on the distance of an object represented in the input video  210  from a view point, for example. As an example, when the object is located relatively far from the observer, the depth of the object may be considered to be greater or ‘deeper’, and in this instance, z may be a value close to 1, for example. Conversely, when the object is located relatively close to the observer, the depth of the object may be considered to be less and ‘shallower’, and in this instance, z may be a value close to 0, also as an example. Here, N indicates a number of a horizontal line of an image of the input video  210 . 
     Referring to Equation 1, the initial depth depends on y coordinates of the image of the input video  210 . In general, in the case of the object represented in the input video  210 , an object located in an upper portion of the input video  210  may be considered to be located farther away than an object located in a lower portion of the input video  210 . 
     Therefore, using such a feature, i.e., the vertical positioning of the object within a frame, the initial depth for that object may be established by determining the depth of the object located in the upper portion of the input video  210  to be deeper than the depth of the object located in the lower portion of the input video  210 . Briefly, it is noted that alternate features, including any extracted feature information, may be used for such initial depth establishment. Further, embodiments of the present invention are not limited to the use of Equation 1, even if such a vertical positioning feature is implemented. 
     When the depth estimation apparatus  220  establishes the initial depth, e.g., through the depth map initialization unit  222 , and stores the initial depth, a user may convert the input video  210  into a 3D video by performing a rendering process for converting the 2D input video  210  into a 3D video using that estimated depth map information. 
     However, since the initial depth established in the depth map initialization unit  222  is not a smoothed depth mapping, e.g., since the initial depth may be based only on such respective two-dimensional positioning of a respective pixel or object of the input video  210 , and not feature information relative to the video, a 3D video which is converted using the initial depth may have inaccurate depths. 
     Therefore, according to the one or more embodiments, the depth estimation apparatus  220  may further assign comparatively more accurate depth information to the input video  210  by appropriately adjusting depths using feature information of the at least one pixel of the input video  210 , based on the initial depth established by the depth map initialization unit  222 . 
     For this, the feature information extraction unit  223  may extract at least one feature information of the input video  210 , and provide the extracted information to the depth update unit  225 . 
     According to one or more embodiments, such feature information extracted in the feature information extraction unit  222  may be at least one particular feature information, such as at least one of edge information, color information, luminance information, motion information, and histogram information, for example, noting that alternatives are equally available. As another example, here, depth estimation from certain features may be accomplished through depth perception, such as monocular cues. The depth perception could include a review based on camera focus blur, vanishing lines, and occlusion areas (e.g., from T-junctions). 
     The depth update unit  225  may calculate a final depth of the input video  210  by performing filtering based on the at least one particular feature information and the initial depth, and update the depth map  224  based on the calculated depth. 
     According to one or more embodiments, the feature information extraction unit  222  may further calculate a weight(s) between at least one pixel of the input video  210  and adjacent pixels based on the at least one particular feature information. 
     Here, according to one or more embodiments, the feature information extraction unit  223  may calculate a weight so as to depend on similarities of feature information between the at least one pixel and the adjacent pixels. 
     Also, according to one or more embodiments, the depth update unit  225  may perform filtering based on the weight(s) calculated in the feature information extraction unit  223 . 
     In the related context, example operations of the feature information extraction unit  225  and the depth update unit  225  will be described in greater detail below. 
     If it is assumed that the feature information extraction unit  223  extracts luminous information of the input video  210 , a weight is then calculated based on the luminous information. 
     The feature information extraction may calculate a weight between at least one pixel of the input video  210  and adjacent pixels based on the similarity of the luminous information. 
     As an example, weights between a pixel ‘a’ and each of pixels ‘x’, ‘y’, ‘z’, and ‘w’ adjacent to the pixel ‘a’ may be calculated. When luminosities of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ are similar to a luminosity of the pixel ‘a’ in orders of pixels ‘x’, ‘y’, ‘z’, and ‘w’, weights may be determined in orders of pixels ‘x’, ‘y’, ‘z’, and ‘w’. 
     The depth update unit  225  may then update the initial depth of the pixel ‘a’ stored in the depth map  224  as the first depth ‘a’ by calculating a first depth ‘a’ of the pixel ‘a’, the first depth ‘a’ being calculated by applying initial depths of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ stored in the depth map  224  according to the weights calculated in the feature information extraction unit  223 . 
     At the same time, in the same manner as the pixel ‘a’, the depth update unit  225  may calculate a first depth ‘b’ of each of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ by considering weights between each of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ and adjacent pixels, and may update initial depths of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ stored in the depth map  224  as the first depth ‘b’. 
     When the initial depths of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ are updated into the first depth ‘b’, the depth update unit  225  may update the first depth ‘a’ of the pixel ‘a’ stored in the depth map  224  as the second depth ‘a’ by calculating a second depth ‘a’ of the pixel ‘a’, the second depth ‘a’ being calculated by applying first depths ‘b’ of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ stored in the depth map  224  according to the weights. 
     In this instance, in the same manner as the pixel ‘a’, the depth update unit  225  may update the first depth ‘b’ of each of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ stored in the depth map  224  as the second depth ‘b’ based on the weights between the first depths ‘b’ of each of the pixels ‘x’, ‘y’, ‘z’, and ‘w’ and each of adjacent pixels. 
     As described above, the depth update unit  225  may filter depths stored in the depth map  224  by repeatedly performing the above described updating operations. 
     Consequently, the depth estimation apparatus  220  may convert the input video  210  into a 3D video having an accurate depth by appropriately adjusting depths based on feature information between at least one pixel of the input video  210  and adjacent pixels, for example. 
     In the related context, the feature information extraction unit  223  may calculate the weights by the below Equation 2, for example. 
     
       
         
           
             
               
                 
                   
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     Here, ω indicates a weight, (x, y) indicates coordinates of at least one pixel of the input video  210 , (x′, y′) indicates coordinates of pixels adjacent to the at least one pixel, Y indicates at least one particular feature information of the input video  210 , and σ indicates a filter parameter, and the depth may be calculated according to the below Equation 3, for example. 
     
       
         
           
             
               
                 
                   
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     Here, z(x, y) indicates a depth of at least one pixel of the input video  210 , z(x′, y′) indicates depths of adjacent pixels adjacent to the at least one pixel, w indicates a weight, and k indicates a normalization factor. 
     As described above, the depth update unit  225  may update depths stored in the depth map  224  according to Equation 3, for example. 
     According to one or more embodiments, the depth map initialization unit  222  may, thus, establish the initial depth using the at least one particular feature information, and store the established initial depth in the depth map  224 . 
     Consequently, the depth map initialization unit  222  may establish an initial depth in which smoothing has be performed. 
     The depth estimation apparatus  220  may reduce complexity of iterated operations that may be performed for the depth update unit  225  to smooth depths stored in the depth map  224  by establishing an initial depth in which smoothing may be performed via the depth map initialization unit  222 . 
     In this instance, according to the one or more embodiments, the depth estimation apparatus  220  may include further feature information extraction units in addition to the feature information extraction unit  223 . 
     Here, the depth map initialization unit  222  may establish the initial depth by receiving at least one particular feature information from another feature extraction information unit. 
     According to one or more embodiments, the depth estimation unit  220  may further include a post-processing unit, the post-processing unit post-processing the depth map  224  to convert the input video  210  into a 3D video having a smoother depth. 
     Also, according to one or more embodiments, the depth estimation apparatus  220  may further include a pre-processing unit  221 . 
     The pre-processing unit  221  may convert a color space of the input video  310  or may extract a motion vector of the input video  210  by decoding the input video  210  when the input video  210  is a video which is encoded into a predetermined video stream. 
     When the pre-processing unit  221  performs a function of converting a color space of the input video  210 , the feature information extraction unit  223  may extract more accurate feature information of the input video  210 . 
     As an example, when the input video  210  is a video consisting of a YCbCr color space or of a red, green and blue color space, the pre-processing unit  221  may convert the color space of the input video  210  into a L*U*V* color space, for example, so that the feature information extraction unit  223  may extract more accurate feature information of the input video  210 . 
     Also, when the pre-processing unit  221  performs a function of extracting a motion vector of the input video  210  by decoding the input video  210 , the feature information extraction unit  223  may extract feature information of the input video  230  using the motion vector. 
       FIG. 3  is a diagram illustrating a structure of a depth estimation apparatus  320 , according to still one or more other embodiments. The depth estimation apparatus  320  may include a feature information extraction unit  322 , an up-scaling unit  325 , and a depth establishment unit  326 , for example. Further, according to one or more embodiments, the depth establishment unit  326  may further include a depth map initialization unit  321  and a depth update unit  324 . 
     The depth map initialization unit  321  may divide a plurality of pixels of an input video  310  into at least one block, establish an initial depth of the at least one block, and store the initial depth of the at least one block in the depth map  323 . 
     As an example, when a plurality of pixels of the input video  310  are a, b, c, d, e, and f, the depth map initialization unit  321  may divide adjacent pixels of the plurality of pixels into at least one block, such as (a, b), (c, d), or (e, f), establish initial depths of the at least one block, and store the initial depths in the depth map  323 . 
     According to one or more embodiments, the input video  310  may be a monocular video. In addition, according to one or more embodiments, the depth map initialization unit  321  may establish the initial depth according to the above Equation 1, for example, or other features, where (x, y) represent coordinates of the at least one block. 
     The feature information extraction unit  322  may extract at least one particular feature information of the input video  310  and provide the extracted at least one particular feature information of the input video  310  to the depth update unit  324 . 
     According to one or more embodiments, the at least one particular feature information of the input video  310  extracted in the feature information extraction unit  322  may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example, noting that alternative embodiments are equally available. 
     The depth update unit  324  may calculate a second depth of the at least one block by performing filtering based on the at least one particular feature information and the initial depth, and update the depth map  323  using the second depth. 
     According to one or more embodiments, the feature information extraction unit  322  may calculate a weight(s) between the at least one block and each of adjacent blocks based on the at least one particular feature information. 
     Here, according to one or more embodiments, the feature information extraction unit  322  may calculate the weight(s) so as to depend on similarities of feature information between the at least one block and each of the adjacent blocks. 
     In this instance, according to one or more embodiments, the feature information extraction unit  322  may calculate the weight according to Equation 2, for example. In this instance, (x, y) represents coordinates of the at least one block, and (x′, y′) represents coordinates of blocks adjacent to the at least one block. 
     Also, according to one or more embodiments, the depth update unit  324  may perform the filtering based on the weight(s) calculated in the feature information extraction unit  322 . 
     In this instance, according to one or more embodiments, the depth update unit  324  may perform the filtering according to Equation 3, for example. Here, (x, y) represents coordinates of the at least one block, and (x′, y′) represents coordinates of blocks adjacent to the at least one block. 
     In an embodiment, the up-scaling unit  325  may further up-scale the updated depth map  323  based on the plurality of pixels. 
     Consequently, the depth estimation apparatus  320 , according to one or more embodiments, may divide the plurality of pixels of the input video  310  into at least one block, generate the depth map  323 , and up-scale the generated depth map  323 , thereby generating a depth map for converting the input video  310  into a 3D video through comparatively simple operations. 
     According to one or more embodiments, the depth map initialization unit  321  may establish an initial depth based on the at least one particular feature information extracted from the feature information extraction unit  322 , and store the initial depth in the depth map  323 . 
     Accordingly, the depth map initialization unit  321  may establish the initial depth in which smoothing may be performed. 
     Consequently, the depth estimation apparatus  320  may reduce complexity of iterated operations that may be performed for the depth update unit  324  to smooth depths stored in the depth map  323  by establishing an initial depth in which smoothing may be performed via the depth map initialization unit  321 . 
     In this instance, according to one or more embodiments, the depth estimation apparatus  320  may further include additional feature information extraction units in addition to the feature information extraction unit  322 . 
     Here, the depth map initialization unit  321  may establish the initial depth by receiving at least one particular feature information from another feature extraction information unit. 
     According to one or more embodiments, the depth estimation unit  220  may further include a post-processing unit, the post-processing unit post-processing the up-scaled depth map to convert the input video  310  into a 3D video having a smoother depth, for example. 
     Also, according to one or more embodiments, the depth estimation apparatus  320  may further include a pre-processing unit. 
     The pre-processing unit may convert a color space of the input video  310 , or extract a motion vector of the input video  310  by decoding the input video  310  when the input video  310  is a video which is encoded into a predetermined video stream, for example. 
     When the pre-processing unit converts the color space of the input video  310 , the feature information extraction unit  322  may further extract more accurate feature information of the input video  310 . 
     As an example, when the input video  310  is a video consisting of a YCbCr color space or of a red, green and blue color space, the pre-processing unit may convert the color space of the input video  310  into a L*U*V* color space, for example, so that the feature information extraction unit  322  may extract more accurate feature information of the input video  310 . 
     Also, when the pre-processing unit performs an operation of extracting a motion vector of the input video  310  by decoding the input video  310 , the feature information extraction unit  322  may extract feature information of the input video  310  using the motion vector, for example. 
       FIG. 4  is a diagram illustrating a structure of a depth estimation apparatus  430 , according to one or more embodiments. The depth estimation apparatus  430  may include a feature information extraction unit  438  and a depth establishment unit  442 , for example. In addition, according to one or more embodiments, the depth establishment unit  442  may further include a depth map initialization unit  437  and a depth update unit  440 . 
     The depth map initialization unit  437  may establish an initial depth of at least one pixel of an input video  410  and store the established initial depth in a depth map  439 . 
     According to one or more embodiments, the input video  410  may be a monocular video. 
     The feature information extraction unit  438  may extract at least one particular feature information of the input video  410  and provide the extracted at least one particular feature information to a depth update unit  440 . 
     According to one or more embodiments, the at least one particular feature information extracted in the feature information extraction unit  438  may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     The depth update unit  440  may calculate a final depth of the input video  410  by performing filtering based on the at least one particular feature information and the initial depth, and update the depth map  439  based on the calculated depth. 
     According to one or more embodiments, the feature information extraction unit  222  may calculate a weight between at least one pixel of the input video  210  and each of adjacent pixels based on the at least one particular feature information. 
     In this instance, according to one or more embodiments, the feature information extraction unit  438  may calculate the weight so as to depend on similarities of feature information between the at least one pixel and the adjacent pixels. 
     Here, according to one or more embodiments, the feature information extraction unit  438  may calculate the weight according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the depth update unit  440  may perform filtering based on the weight. 
     In this instance, according to one or more embodiments, the depth update unit  440  may perform the filtering according to the aforementioned Equation 3, for example. 
     As described above, when the depth map initialization unit  437  establishes the initial depth, the depth estimation apparatus  430  may establish a depth of the input video  410 , resulting in the depth update unit  440  updating the depth map  439  using the at least one particular feature information. 
     The depth estimation apparatus  420  may simplify operations for estimating the depth of the input video  410  by down-scaling the input video  430 , estimating a second depth of the down-scaled input video, and using the second depth as the initial depth, for example. 
     In the related context, the depth estimation apparatus  430  may further include a down-scaling unit  431 , a second feature information extraction unit  433 , a second depth establishment unit  441 , and an up-scaling unit  436 , for example. 
     Here, according to one or more embodiments, the second depth establishment unit  441  may include a second depth map initialization unit  432  and a second depth update unit  435 , for example. 
     The down-scaling unit  431  may down-scale the input video  410  received in the depth estimation apparatus  430  to a predetermined resolution. 
     As an example, when a resolution of the input video is 1024×768, the downscaling unit  431  may down-scale the resolution of the input video  410  into 800×600. 
     The second depth map initialization unit  432  may establish a second initial depth of at least one pixel of an input video  420 , which is down-scaled by the down-scaling unit  431 , and store the second initial depth in the second depth map  434 . 
     According to one or more embodiments, the second depth map initialization unit  432  may calculate the second initial depth according to the aforementioned Equation 1, or other features, for example. 
     The second feature information extraction unit  433  may extract at least one particular second feature information of the down-scaled input video  420  and provide the extracted at least one particular second feature information of the down-scaled input video  420  to the second depth update unit  435 . 
     According to one or more embodiments, the at least one particular second feature information extracted in the second feature information extraction unit  433  may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     The second depth update unit  435  may calculate a second depth of the down-scaled input video  420  by performing filtering based on the at least one particular second feature information and the second initial depth, and update the second depth map  434  using the second depth. 
     According to one or more embodiments, the second feature information extraction unit  433  may calculate a second weight between at least one pixel of the down-scaled input video  420  and each of adjacent pixels based on the at least one particular second feature information. 
     According to one or more embodiments, the second feature information extraction unit  433  may calculate the second weight so as to depend on similarities of feature information between the at least one pixel and adjacent pixels. 
     Here, according to one or more embodiments, the second feature information extraction unit  433  may calculate the second weight according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the second depth update unit  435  may perform the filtering based on the second weight. 
     Here, according to one or more embodiments, the second depth update unit  435  may perform the filtering according to the aforementioned Equation 3, for example. 
     The up-scaling unit  436  may up-scale the updated second depth map  434  based on a resolution of the input video  410  and provide the up-scaled depth map  434  to the depth map initialization unit  437 . 
     Here, according to one or more embodiments, the up-scaling unit  436  may up-scale the second depth map  434  based on the at least one particular second feature information and the at least one particular feature information. 
     When the up-scaling unit  436  up-scales the second depth map  434 , the depth map initialization unit  437  establishes a depth stored in the up-scaled second depth map as an initial depth of the at least one pixel of the input video  410 , and stores the initial depth of the at least one pixel in the depth map  439 . 
     As described above, the depth estimation unit  430  establishes a depth in which filtering may be performed as the initial depth, thereby reducing complexity of operations that may be performed for the depth update unit  440  to update the depth map  439  by filtering a depth of the input video  410 . 
     Consequently, the depth estimation apparatus  430  may reduce overall operations for estimating the depth of the input video  410  by down-scaling the input video  410  and generating a depth map using a combination of access methods which generate an initial depth map. 
     Here, the depth estimation apparatus  430  is illustrated in  FIG. 4  as generating the depth map by down-scaling the input video  410  once, however embodiments are not limited to this. 
     That is, according one or more embodiments, the depth estimation apparatus  430  down-scales the input video  430  n times, generates a depth map from an n th  down-scaled input video and up-scales the generated depth map, thereby using as an initial depth map for generating a depth map of an (n−1) th  down-scaled input video. Also, in the case of a depth map of the (n−1) th  down-scaled input video, the depth map of the (n−1) th  down-scaled input video is up-scaled, thereby using as an initial depth map for generating a depth map of an (n−2) th  down-scaled input video, as only an example. 
     Consequently, the depth estimation apparatus  430  may simplify operations for estimating the depth of the input video  410  by using a depth map, generated by repeatedly performing the above described operations, as the initial depth map for generating the depth map of the input video  410 . 
     Above, various embodiments regarding the depth estimation apparatus have been described with reference to  FIGS. 1 through 4 . Hereinafter, various embodiments regarding a 3D video conversion apparatus which converts an input video into a 3D video using the depth estimation apparatus is described with reference to  FIGS. 5 through 8 . 
       FIG. 5  is a diagram illustrating a structure of a 3D video conversion apparatus  520 , according to one or more embodiments. The 3D video conversion apparatus  520  may include a feature information extraction unit  521 , a depth establishment unit  522 , and a rendering unit  523 , for example. 
     When an input video  510  is provided to the 3D video conversion apparatus  520 , a feature extraction unit  521  may extract at least one particular feature information of the input video  510  and provide the extracted at least one particular feature information to a depth establishment unit  522 . 
     According to one or more embodiments, the input video may be a monocular video. 
     Also, according to one or more embodiments, the feature information may be at least one of edge information, color information, luminance information, motion information, and histogram information, for example. 
     The depth establishment unit  522  may establish a depth of the input video  510  based on the at least one particular feature information received from the feature information extraction unit  521 . 
     The 3D video conversion apparatus  520  may convert the input video  510  into a 3D video by establishing the depth of the input video  510  based on the at least one particular feature information of the input video  510  and by rendering the 2D input video  510  using the depth. 
     According to one or more embodiments, the 3D video conversion apparatus  520  may be expanded in various embodiments in addition to the embodiments illustrated in  FIG. 5 . Various example embodiments of the 3D video conversion apparatus  520  are described with reference to  FIGS. 6 through 8 , noting that alternatives are equally available. 
       FIG. 6  is a diagram illustrating a structure of a 3D video conversion apparatus  620 , according to one or more other embodiments. The 3D video conversion apparatus  620  may include a feature information extraction unit  622 , a rendering unit  625 , and a depth establishment unit  626 , for example. According to one or more embodiments, the depth establishment unit  626  may further include a depth map initialization unit  621  and a depth update unit  624 , for example. 
     When the input video  610  is provided to the 3D video conversion apparatus  620 , the depth map initialization unit  621  may establish an initial depth of at least one pixel of the input video  610 , and store the initial depth in the depth map  623 . 
     According to one or more embodiments, the input video  610  may be a monocular video. 
     Also, according to one or more embodiments, the depth map initialization unit  621  may establish the initial depth of each frame of a sequence of the input video  610 , and store the initial depth in the depth map  623 . 
     Also, according to one or more embodiments, the depth map initialization unit  621  may establish the initial depth according to the aforementioned Equation 1, or other features, for example. 
     The feature information extraction unit  622  may extract at least one particular feature information of the input video  610  and provide the extracted at least one particular feature information of the input video  610  to the depth update unit  624 . 
     According to one or more embodiments, the at least one particular feature information may be at least one of edge information, color information, luminance information, motion information, and histogram information, for example. 
     The depth update unit  624  may calculate a final depth of the input video  610  by performing filtering based on the at least one particular feature information and the initial depth, and update the depth map  623  using the calculated depth. 
     According to one or more embodiments, the feature information extraction unit  622  may calculate a weight between at least one pixel of the input video  610  and adjacent pixels based on the at least one particular feature information. 
     Also, according to one or more embodiments, the feature information extraction unit  622  may calculate the weight so as to depend on similarities of feature information between the at least one pixel and the adjacent pixels. 
     Here, according to one or more embodiments, the feature information extraction unit  622  may calculate the weight according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the depth update unit  624  may perform filtering based on the weight. 
     Here, according to one or more embodiments, the depth update unit  624  may perform the filtering according to the aforementioned Equation 3, for example. 
     The rendering unit  625  may render the input video  610  into a 3D video using the depth map  623  which is updated by the depth update unit  624 . 
     According to one or more embodiments, the depth map initialization unit  621  may establish the initial depth using the at least one particular feature information, and store the initial depth in the depth map  623 . 
     Accordingly, the depth map initialization unit  621  may establish an initial depth in which smoothing may be performed. 
     Consequently, the 3D video conversion unit  620  may reduce complexity of iterated operations that may be performed for the depth update unit  624  to smooth depths stored in the depth map  623  by establishing an initial depth in which smoothing may be performed via the depth map initialization unit  621 . 
     In this instance, according to one or more embodiments, the 3D video conversion apparatus  620  may further include additional feature information extraction units in addition to the feature information extraction unit  622 . 
     Here, the depth map initialization unit  621  may establish the initial depth by receiving the at least one particular feature information from another feature information extraction unit. 
     According to one or more embodiments, the 3D video conversion unit  620  may further include a post-processing unit, the post-processing unit post-processing the input video  610  to be converted into a 3D video having a smoother depth. 
     Also, according to one or more embodiments, the 3D video conversion apparatus  620  may further include a pre-processing unit. 
     The pre-processing unit may convert a color space of the input video  610 , or extract a motion vector of the input video  610  by decoding the input video  610  when the input video  610  is a video which is encoded into a predetermined video stream. 
     When the pre-processing unit performs a function of converting the color space of the input video  610 , the feature information extraction unit  622  may extract more accurate feature information of the input video  610 . 
     As an example, when the input video  610  is a video consisting of a YCbCr color space or of a red, green and blue color space, the pre-processing unit may convert the color space of the input video  610  into a L*U*V* color space so that the feature information extraction unit  622  may extract more accurate feature information of the input video  610 . 
     Also, when the pre-processing unit performs a function of extracting a motion vector of the input video  610  by decoding the input video  610 , the feature information extraction unit  622  may extract feature information of the input video  610  using the motion vector, for example. 
       FIG. 7  is a diagram illustrating a structure of a 3D video conversion apparatus  720 , according to still one or more other embodiments. 
     The 3D video conversion apparatus  720  may include a feature information extraction unit  722 , an up-scaling unit  725 , a rendering unit  726 , and a depth establishment unit  727 , for example. 
     According to one or more embodiments, the depth establishment unit  727  may include a depth map initialization unit  721  and a depth update unit  724 , for example. 
     The depth map initialization unit  721  may establish an initial depth of at least one block by dividing a plurality of pixels of an input video  710  into at least one block, and store the initial depth in the depth map  723 . 
     As an example, when a plurality of pixels of the input video  710  are a, b, c, d, e, and f, the depth map initialization unit  721  may divide adjacent pixels of the plurality of pixels into at least one bock, such as (a, b), (c, d), or (e, f), establish initial depths of the at least one block, and store the initial depths in the depth map  723 . 
     According to one or more embodiments, the input video  710  may be a monocular video. 
     Also, according to one or more embodiments, the depth map initialization unit  721  may establish the initial depth according to the aforementioned Equation 1, or other features, for example. 
     Here, (x, y) indicates coordinates of the at least one block. 
     The feature information extraction unit  722  may extract at least one particular feature information of the input video  710  and provide the extracted at least one particular feature information of the input video  610  to the depth update unit  724 . 
     According to one or more embodiments, the at least one particular feature information of the input video  710  extracted in the feature information extraction unit  722  may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     The depth update unit  724  may calculate a second depth of the at least one block by performing filtering based on the at least one particular feature information and the initial depth, and update the depth map  723  using the second depth. 
     According to one or more embodiments, the feature information extraction unit  722  may calculate a weight between the at least one block and each of adjacent blocks based on the at least one particular feature information. 
     Here, according to one or more embodiments, the feature information extraction unit  722  may calculate the weight so as to depend on similarities of feature information between the at least one block and each of the adjacent blocks. 
     In this instance, according to one or more embodiments, the feature information extraction unit  722  may calculate the weight according to the aforementioned Equation 2, for example. 
     Here, (x, y) indicates coordinates of the at least one block, and (x′, y′) indicates coordinates of blocks adjacent to the at least one block. 
     Also, according to one or more embodiments, the depth update unit  724  may perform the filtering based on the weight. 
     In this instance, according to one or more embodiments, the depth update unit  724  may perform the filtering according to the aforementioned Equation 3, for example. 
     Here, (x, y) indicates coordinates of the at least one block, and (x′, y′) indicates coordinates of blocks adjacent to the at least one block. 
     The up-scaling unit  725  may up-scale the updated depth map  723  based on the plurality of pixels. 
     The rendering unit  726  may render the input video  710  into a 3D video using the up-scaled depth map. 
     Consequently, the depth estimation apparatus  720  may divide the plurality of pixels of the input video  710  into at least one block, generate the depth map  723 , and up-scale the generated depth map  723 , thereby converting the input video  710  into a 3D video through comparatively simple operations. 
     According to one or more embodiments, the depth map initialization unit  721  may establish an initial depth based on the at least one particular feature information, and store the initial depth in the depth map  723 . 
     Accordingly, the depth map initialization unit  721  may establish an initial depth in which smoothing may be performed. 
     Consequently, the depth estimation apparatus  720  may reduce complexity of iterated operations that may be performed for the depth update unit  724  to smooth depths stored in the depth map  723  by establishing an initial depth in which smoothing may be performed via the depth map initialization unit  721 . 
     In this instance, according to one or more embodiments, the depth estimation apparatus  720  may further include additional feature information extraction units in addition to the feature information extraction unit  722 . 
     Here, the depth map initialization unit  721  may establish the initial depth by receiving at least one particular feature information from another feature extraction information unit. 
     According to one or more embodiments, the 3D video conversion unit  720  may further include a post-processing unit, the post-processing unit post-processing up-scaled depth map to convert the input video  710  into a 3D video having a smoother depth. 
     Also, according to one or more embodiments, the depth estimation apparatus  720  may further include a pre-processing unit. 
     The pre-processing unit may convert a color space of the input video  710  or may extract a motion vector of the input video  710  by decoding the input video  710  when the input video  710  is a video which is encoded into a predetermined video stream. 
     When the pre-processing unit  221  performs a function of converting a color space of the input video  210 , the feature information extraction unit  223  may extract more accurate feature information of the input video  210 . 
     When the pre-processing unit performs a function of converting the color space of the input video  710 , the feature information extraction unit  722  may extract more accurate feature information of the input video  710 . 
     As an example, when the input video  710  is a video consisting of a YCbCr color space or of a red, green and blue color space, the pre-processing unit may convert the color space of the input video  710  into a L*U*V* color space so that the feature information extraction unit  722  may extract more accurate feature information of the input video  710 . 
     Also, when the pre-processing unit performs a function of extracting a motion vector of the input video  710  by decoding the input video  710 , the feature information extraction unit  722  may extract feature information of the input video  710  using the motion vector. 
       FIG. 8  is a diagram illustrating a structure of a 3D video conversion apparatus  830 , according to yet one or more other embodiments. The 3D video conversion apparatus  830  may include a feature information extraction unit  838 , a depth establishment unit  843 , and a rendering unit  841 , for example. According to one or more embodiments, the depth establishment unit  843  may further include a depth map initialization unit  837  and a depth update unit  840 , for example. 
     The depth map initialization unit  837  may establish an initial depth of at least one pixel of an input video  810  and store the initial depth of at least one pixel of an input video  810  in the depth map  839 . 
     According to one or more embodiments, the input video  810  may be a monocular video. 
     The feature information extraction unit  838  may extract at least one particular feature information of the input video  810  and provide the at least one particular feature information of the input video  810  to the depth update unit  840 . 
     According to one or more embodiments, the at least one particular feature information of the input video  810  may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     The depth update unit  840  may calculate a depth of the input video  810  by performing filtering based on the at least one particular feature information and the initial depth, and update the depth map  839  using the depth. 
     According to one or more embodiments, the feature information extraction unit  838  may calculate a weight between the at least one pixel of the input video  810  and each of adjacent pixels based on the at least one particular feature information. 
     Here, according to one or more embodiments, the feature information extraction unit  838  may calculate the weight so as to depend on similarities of feature information between the at least one pixel and each of the adjacent pixels. 
     In this instance, according to one or more embodiments, the feature information extraction unit  838  may calculate the weight according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the depth update unit  840  may perform the filtering based on the weight. 
     Here, according to one or more embodiments, the depth update unit  840  may perform the filtering according to the aforementioned Equation 3, for example. 
     The rendering unit  841  may render the input video  810  into a 3D video using an updated depth map  839 . 
     As described above, the depth estimation apparatus  830  may estimate the depth of the input video  810  by updating the depth map  839  using the at least one particular feature information when the depth map initialization unit  837  establishes the initial depth. 
     Also, according to one or more embodiments, the depth estimation apparatus  830  may simplify operations for estimating the depth of the input video  810  by down-scaling the input video  810 , estimating a second depth of the down-scaled input video, and using the second depth as the initial depth. 
     In the related context, the depth estimation apparatus  830  may further include a down-scaling unit  831 , a second feature information extraction unit  833 , a second depth establishment unit  842 , and an up-scaling unit  836 , for example. 
     According to one or more embodiments, the second depth establishment unit  842  may include a second depth map initialization unit  832  and a second depth update unit  835 , for example. 
     The down-scaling unit  831  may down-scale the input video  810  received in the 3D video conversion apparatus  830  to a predetermined resolution. 
     As an example, when a resolution of the input video is 1024×768, the downscaling unit  831  may down-scale the resolution of the input video  810  to 800×600. 
     According to one or more embodiments, the input video  810  may be a monocular video. 
     The second depth map initialization unit  832  may establish a second depth of the at least one pixel of an input video  820  which is down-scaled by the down-scaling unit  831  and store the second initial depth in the second depth map  834 . 
     According to one or more embodiments, the second depth map initialization unit  832  may establish the second initial depth according to the aforementioned Equation 1, for example, or other features. 
     The second feature information extraction unit  833  may extract at least one particular second feature information of the down-scaled input video  820  and provide the extracted at least one particular second feature information of the down-scaled input video  820  to the second depth update unit  835 . 
     According to one or more embodiments, the at least one particular second feature information extracted by the second feature information extraction unit  833  may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     The second depth update unit  835  may calculate a second depth of the down-scaled input video  820  by performing filtering based on the at least one particular second feature information and the second initial depth, and update the second depth map  834  using the second depth. 
     According to one or more embodiments, the second feature information extraction unit  833  may calculate a second weight between at least one pixel of the down-scaled input video  820  and each of adjacent pixels based on the at least one particular second feature information. 
     According to one or more embodiments, the second feature information extraction unit  833  may calculate the second weight so as to depend on similarities of feature information between the at least one pixel and adjacent pixels. 
     Here, according to one or more embodiments, the second feature information extraction unit  833  may calculate the second weight according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the second depth update unit  835  may perform the filtering based on the second weight. 
     Here, according to one or more embodiments, the second depth update unit  835  may perform the filtering according to the aforementioned Equation 3, for example. 
     The up-scaling unit  836  may up-scale the updated second depth map  834  based on a resolution of the input video  810  and provide the up-scaled depth map  834  to the depth map initialization unit  837 . 
     Here, according to one or more embodiments, the up-scaling unit  836  may up-scale the second depth map  834  based on the at least one particular second feature information and the at least one particular feature information, and provide the up-scaled second depth map to the depth map initialization unit  837 . 
     When the up-scaling unit  836  up-scales and provides the second depth map  834 , the depth map initialization unit  837  may establish a depth stored in the up-scaled second depth map as an initial depth of the at least one pixel of the input video  810 , and store the initial depth in the depth map  839 . 
     As described above, by establishing a depth in which filtering may be performed as the initial depth, complexity of operations that may be performed for the depth update unit  840  to update the depth map  839  by filtering a depth of the input video  810  may be reduced. 
     Consequently, the 3D video conversion apparatus  830  may reduce overall operations for estimating the depth of the input video  810  by down-scaling the input video  810  and generating a final depth map using a combination of access methods which generate an initial depth map. 
     Here, the 3D video conversion apparatus  830  is illustrated in  FIG. 8  as generating the depth map by down-scaling the input video  810  once, however embodiments are not limited to this. 
     That is, according one or more embodiments, the 3D video conversion apparatus  830  down-scales the input video  810  n times, generates a depth map from an n th  down-scaled input video and up-scales the generated depth map, thereby being used as an initial depth map for generating a depth map of an (n−1) th  down-scaled input video, for example. Also, in the case of a depth map of the (n−1) th  down-scaled input video, the depth map of the (n−1) th  down-scaled input video is up-scaled, thereby using as an initial depth map for generating a depth map of an (n−2) th  down-scaled input video, also as an example. 
     Consequently, the 3D video conversion apparatus  430  may simplify operations for estimating the depth of the input video  810  by using a depth map, generated by repeatedly performing the above described operations, as the initial depth map for generating the depth map of the input video  810 . 
       FIG. 9  is flowchart illustrating a depth estimation method, according to one or more embodiments. 
     In operation S 910 , at least one particular feature information of an input video is extracted. 
     According to one or more embodiments, the at least one particular feature information may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     In operation S 920 , a depth of the input video is established based on the at least one particular feature information extracted in operation S 910 . 
     According to one or more embodiments, the depth estimation method may be expanded in various embodiments in addition to the method illustrated in  FIG. 9 . Therefore, example various embodiments regarding the depth estimation method are described with reference to  FIGS. 10 through 12 , noting that additional embodiments are equally available. 
       FIG. 10  is a flowchart illustrating a depth estimation method, according to one or more other embodiments. 
     In operation S 1010 , at least one particular feature information of an input video is extracted. 
     The at least one particular feature information of the input video may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     In operation S 1020 , an initial depth of at least one pixel is established and the initial depth is stored in a depth map. 
     According to one or more embodiments, the initial depth may be established according to the aforementioned Equation 1, for example, or other features, in operation S 1020 . 
     In operation S 1030 , a final depth of the input video is calculated by performing filtering based on the at least one particular feature information and the initial depth, and the depth map is updated using the depth. 
     According to one or more embodiments, operation S 1010  may include an operation of calculating a weight between the at least one pixel and adjacent pixels based on the at least one particular feature information based on the at least one particular feature information in operation S 1010 . 
     Here, according to one or more embodiments, the weight may be calculated according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the filtering may be performed based on the weight in operation S 1030 . 
     Here, according to one or more embodiments, the filtering may be performed according to the aforementioned Equation 3, for example, in operation S 1030 . 
       FIG. 11  is a flowchart illustrating a depth estimation method, according to still one or more other embodiments. 
     In operation S 1110 , a plurality of pixels of the input video are divided into at least one block, an initial depth of the at least one block is established, and the initial depth is stored in a depth map. 
     According to one or more embodiments, the initial depth may be established according to the aforementioned Equation 1, for example, or other features, in operation S 1110 . 
     Here, (x, y) indicates coordinates of the at least one block. 
     In operation S 1120 , at least one particular feature information of the at least one block is extracted. 
     According to one or more embodiments, the at least one particular feature information of the at least one block may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     In operation S 1130 , a second depth of the at least one block is calculated by performing filtering based on the at least one particular feature information and the initial depth, and the depth map is updated using the second depth. 
     According to one or more embodiments, operation S 1120  may include an operation of calculating a weight between the at least one block and adjacent blocks based on the at least one particular feature information. 
     In this instance, according to one or more embodiments, the weight may be calculated according to the aforementioned Equation 2, for example. 
     Here, (x, y) indicates coordinates of the at least one block, and (x′, y′) indicates coordinates of blocks adjacent to the at least one block. 
     Also, according to one or more embodiments, the filtering may be performed based on the weight in operation S 1130 . 
     In this instance, according to one or more embodiments, the filtering may be performed according to the aforementioned Equation 3, for example, in operation S 1130 . 
     Here, (x, y) indicates coordinates of the at least one block, and (x′, y′) indicates coordinates of blocks adjacent to the at least one block. 
     In operation S 1140 , the updated depth map is up-scaled based on the plurality of pixels. 
       FIG. 12  is a flowchart illustrating a depth estimation method, according to yet one or more other embodiments. 
     In operation S 1210 , an input video is down-scaled into a predetermined resolution. 
     In operation S 1220 , a second initial depth of at least one pixel of the down-scaled input video is established, and the second initial depth is stored in a second depth map. 
     According to one or more embodiments, the second initial depth may be established according to the aforementioned Equation 1, for example, or other features, in operation S 1220 . 
     In operation S 1230 , at least one second particular feature information of the down-scaled input video is extracted. 
     According to one or more embodiments, the at least one particular second feature information may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     In operation S 1240 , a second depth of the down-scaled input video may be calculated by performing filtering based on the at least one particular second feature information and the second initial depth is calculated, and the second depth map is updated using the second depth. 
     According to one or more embodiments, operation  1230  may include an operation of calculating a second weight between the at least one pixel and the adjacent pixels based on the at least one particular second feature information. 
     In this instance, according to one or more embodiments, the second weight may be calculated according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the filtering may be performed based on the second weight in operation S 1240 . 
     In this instance, according to one or more embodiments, the filtering may be performed according to the aforementioned Equation 3, for example, in operation S 1240 . 
     In operation S 1250 , the updated second depth map is up-scaled based on a resolution of the input video. 
     In operation S 1260 , at least one particular feature information of the input video is extracted. 
     According to one or more embodiments, the at least one particular feature information may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     In operation S 1270 , a depth stored in the up-scaled second depth map is established as initial depth of at least one pixel of the input video, and the initial depth of at least one pixel is stored in the depth map. 
     In operation S 1280 , a final depth of the input video is calculated by performing filtering based on the at least one particular feature information and the initial depth, and the depth map is updated using the depth. 
     According to one or more embodiments, operation S 1260  may include an operation of calculating a weight between the at least one pixel and adjacent pixels based on at the least one feature information. 
     In this instance, according to one or more embodiments, the weight may be calculated according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the filtering may be performed based on the weight in operation S 1280 . 
     In this instance, according to one or more embodiments, the filtering may be performed according to the aforementioned Equation 3, for example, in operation S 1280 . 
     Various embodiments regarding the depth estimation method have been described with reference to  FIGS. 9 through 12 . Hereinafter, various embodiments regarding a 3D video conversion method which converts an input video into a 3D video using the depth estimation method are described with reference to  FIGS. 13 through 16 , noting that further embodiments are equally available. 
       FIG. 13  is a flowchart illustrating a 3D video conversion method, according to one or more embodiments. 
     In operation, S 1310 , at least one particular feature information of an input video is extracted. 
     According to one or more embodiments, the at least one particular feature information may be at least one of edge information, color information, luminance information, motion information, and histogram information, for example. 
     In operation S 1320 , a depth of the input video is established based on the at least one particular feature information extracted in operation S 1310 . 
     In operation S 1330 , the input video is rendered into a 3D video using the filtered depth. 
     According to one or more embodiments, the 3D video conversion method may be variously expanded, in addition to the method illustrated in  FIG. 13 . Hereinafter, the various example embodiments regarding the 3D video conversion method are described with reference to  FIGS. 14 through 16 , noting that alternative embodiments are equally available. 
       FIG. 14  is a flowchart illustrating a 3D video conversion method, according to one or more other embodiments. 
     In operation S 1410 , at least one particular feature information of an input video is extracted. 
     According to one or more embodiments, the at least one particular feature information of the input video may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     In operation S 1420 , an initial depth of at least one pixel of the input video is established and the initial depth is stored in a depth map. 
     In operation S 1430 , a final depth of the input video is calculated by performing filtering based on the at least one particular feature information and the initial depth, and the depth map is updated using the depth. 
     According to one or more embodiments, operation S 1410  may include an operation of calculating a weight between the at least one pixel and adjacent pixels based on the at least one particular feature information. 
     Here, according to one or more embodiments, the weight may be calculated according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the filtering may be performed based on the weight in operation S 1430 . 
     Here, according to one or more embodiments, the filtering may be performed according to the aforementioned Equation 3, for example. 
     In operation S 1440 , the input video is rendered into a 3D video using the updated depth map. 
       FIG. 15  is a flowchart illustrating a 3D video conversion method, according to still one or more other embodiments. 
     In operation S 1510 , a plurality of pixels of an input video are divided into at least one block, an initial depth of the at least one block is established, and the initial depth is stored in a depth map 
     According to one or more embodiments, the initial depth may be established according to the aforementioned Equation 1, for example, or other features, in operation S 1510 . 
     Here, (x, y) indicates coordinates of the at least one block. 
     In operation S 1520 , at least one particular feature information of the at least one block is extracted. 
     According to one or more embodiments, the at least one particular feature information of the at least one block may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     In operation S 1530 , a second depth of the at least one block is calculated by performing filtering based on at least one particular feature information and the initial depth, and the depth map is updated using the second depth. 
     According to one or more embodiments, operation S 1520  may include an operation of calculating a weight between the at least one block and adjacent blocks based on the at least one particular feature information. 
     Here, according to one or more embodiments, the weight may be calculated according to the aforementioned Equation 2, for example. 
     Here, (x, y) indicates coordinates of the at least one block, and (x′, y′) indicates coordinates of blocks adjacent to the at least one block. 
     Also, according to one or more embodiments, the filtering may be performed based on the weight in operation S 1530 . 
     Here, according to one or more embodiments, the filtering may be performed according to the aforementioned Equation 3, for example, in operation S 1530 . 
     Here, (x, y) indicates coordinates of the at least one block, and (x′, y′) indicates coordinates of blocks adjacent to the at least one block. 
     In operation S 1540 , the updated depth map is up-scaled based on the plurality of pixels. 
     In operation S 1550 , the input video is rendered into a 3D video using the up-scaled depth map. 
       FIG. 16  is a flowchart illustrating a 3D video conversion method, according to one or more other embodiments. 
     In operation S 1610 , an input video is down-scaled into a predetermined resolution. 
     In operation S 1620 , a second initial depth of at least one pixel of the down-scaled input video is established, and the second initial depth is stored in a second depth map. 
     According to one or more embodiments, the second initial depth may be established according to the aforementioned Equation 1, for example, or other features, in operation S 1620 . 
     In operation S 1630 , at least one particular second feature information of the down-scaled input video is extracted. 
     According to one or more embodiments, at least one particular second feature information may be at least one of edge information, color information, luminance information, motion information, or histogram information, for example. 
     In operation S 1640 , a second depth of the down-scaled input video is calculated by performing filtering based on the at least one particular second feature information and the second initial depth, and the second depth map is updated using the second depth. 
     According to one or more embodiments, operation S 1630  may include an operation of calculating a second weight between the at least one pixel and adjacent pixels based on the at least one particular second feature information. 
     In this instance, according to one or more embodiments, the second weight may be calculated according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the filtering may be performed based on the second weight in operation S 1640 . 
     According to one or more embodiments, the filtering may be performed according to the aforementioned Equation 3, for example, in operation S 1640 . 
     In operation S 1650 , the updated second depth map is up-scaled based on a resolution of the input video. 
     In operation S 1660 , at least one particular feature information of the input video is extracted. 
     According to one or more embodiments, the at least one feature particular information may be at least one of edge information, color information, luminance information, motion information, and histogram information, for example. 
     In operation S 1670 , a depth stored in the up-scaled second depth map is established as an initial depth of at least one pixel of the input video, and the initial depth of the at least one pixel is stored in a depth map. 
     In operation S 1680 , a final depth of the input video is calculated by performing filtering based on the at least one particular feature information and the initial depth, and the depth map is updated using the depth. 
     According to one or more embodiments, operation S 1660  may include an operation of calculating a weight between the at least one pixel and adjacent pixels based on the at least one particular feature information. 
     Here, according to one or more embodiments, the weight may be calculated according to the aforementioned Equation 2, for example. 
     Also, according to one or more embodiments, the filtering may be performed based on the second weight in operation S 1680 . 
     Here, according to one or more embodiments, the filtering may be performed according to the aforementioned Equation 3, for example. 
     In operation S 1690 , the input video is rendered into a 3D video using the updated depth map. 
     In addition to the above, one or more embodiments may include products accomplishing the same, such as 3D displays (e.g., televisions, monitors, mobile devices, etc.) and studio post-production systems, for example. The estimated depth may equally be beneficial for other devices, such as in enhancing traditional image processing systems and frame rate conversion devices. Estimated depth may also be an additional variable for algorithms for computer vision applications, including object detection, recognition, and even encoding in an encoder preferentially encoding slices or objects of an image or frames before other slices or objects, for example. 
     The depth estimation method and 3D video conversion method according to one or more of the above-described embodiments may be recorded in computer-readable media including computer readable code or instructions to implement various operations embodied by a computer or implementable by one or more processing devices. The media may also include, in combination with the computer readable code, data files, data structures, and the like. Examples of computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD ROM disks and DVDs; magneto-optical media such as optical disks; and hardware devices that are specially configured to store and perform instructions, such as read-only memory (ROM), random access memory (RAM), flash memory, and the like. Examples of such coding or instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer or one or more processing devices using an interpreter, for example. 
     While aspects of the present invention has been particularly shown and described with reference to differing embodiments thereof, it should be understood that these exemplary embodiments should be considered in a descriptive sense only and not for purposes of limitation. Descriptions of features or aspects within each embodiment should typically be considered as available for other similar features or aspects in the remaining embodiments. 
     Thus, although a few embodiments have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the claims and their equivalents.