CN105163111B - Method for evaluating visual comfort of three-dimensional image based on classification of scene modes - Google Patents

Abstract

The invention discloses a method for evaluating the visual comfort of a three-dimensional image based on classification of scene modes. The method comprises the following steps: determining ten kinds of scene modes of the three-dimensional image according to depth positions, where a foreground object and a background area in a natural scene are; then, determining the scene mode, which each three-dimensional image belongs to, in a three-dimensional image database, and establishing a visual comfort evaluation mode in each scene mode; and firstly determining the scene mode, which the three-dimensional image to be evaluated belongs to, then, selecting the visual comfort evaluation mode in the scene mode, calculating a visual comfort evaluation predication value of the three-dimensional image to be evaluated, and finally, correcting the visual comfort evaluation predication value of the three-dimensional image to be evaluated. The method disclosed by the invention has the advantages that: perception of a human visual system to the visual comfort of the three-dimensional image can be precisely reflected through the visual comfort evaluation modes respectively established based on ten kinds of scenes; and thus, the consistency between an objective evaluation result and human eye subjective perception can be effectively improved.

Description

A kind of stereo image vision comfort level evaluation method based on scene mode classification

Technical field

The present invention relates to a kind of stereo-picture Quality of experience evaluation method, especially relate to a kind of scene mode that is based on and classify Stereo image vision comfort level evaluation method.

Background technology

With the fast development of Stereoscopic Video Presentation technology and high-quality stereoscopic video content acquiring technology, three-dimensional video-frequency Visual experience quality (qoe, quality of experience) is one of three-dimensional video-frequency system design major issue, and Visual comfort (vc, visual comfort) is one of key factor of visual experience quality of impact three-dimensional video-frequency.At present, Research contents distortion is concentrated mainly on for visual experience quality to the visual experience matter quantifier elimination of three-dimensional video-frequency/stereo-picture Impact, this respect created many achievements in research.The research of stereo image vision comfort level is in stereoscopic image content not The physiological discomfort causing under the premise of distortion, the visual comfort objective evaluation model obtaining is for raising beholder's Visual experience quality, the making instructing 3d content and later stage process and have a very important role.

Existing stereo image vision comfort level evaluation method is mainly passed through to extract the left and right visual point image of stereo-picture And the feature of anaglyph, such as parallax, gradient etc., then set up using the algorithm of method statistically or machine learning Play objective models.But at present to causing the various factors of physiologically discomfort to make clear of not yet in the content of stereo-picture Thorough, lead to the uniformity between objective evaluation result and human eye subjective perception poor.Relax for obtaining the preferable vision of evaluation effect Appropriate evaluation method, needs all kinds of natural scenes are finely divided, and on this basis the various characteristics of image of Accurate Analysis to regarding Feel the influence degree of comfort level.

Content of the invention

The technical problem to be solved is to provide a kind of stereo-picture euphorosia based on scene mode classification Degree evaluation method, it can effectively improve the uniformity between objective evaluation result and human eye subjective perception.

The technical scheme that present invention solution above-mentioned technical problem is adopted is: a kind of stereogram based on scene mode classification As visual comfort evaluation method it is characterised in that comprising the following steps:

1. 10 kinds of scene modes of the natural scene shown by three-dimensional display are determined, the 1st kind of scene mode is stereogram Foreground target in the right viewpoint parallax gray level image of picture convex in screen and be in non-comfort zone, background region recessed in screen and place In non-comfort zone；2nd kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and Be in non-comfort zone, background region convex in screen and be in non-comfort zone；3rd kind of scene mode is that the right viewpoint of stereo-picture regards Foreground target in difference gray level image recessed in screen and be in non-comfort zone, background region recessed in screen and be in non-comfort zone； 4th kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and be in non-comfortable Area, background region recessed in screen and be in comfort zone；5th kind of scene mode is in the right viewpoint parallax gray level image of stereo-picture Foreground target convex in screen and be in non-comfort zone, background region convex in screen and be in comfort zone；6th kind of scene mode be Foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and be in comfort zone, background region recessed in screen And it is in non-comfort zone；7th kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture recessed in screen Curtain and be in comfort zone, background region recessed in screen and be in non-comfort zone；8th kind of scene mode is the right viewpoint of stereo-picture Foreground target in parallax gray level image convex in screen and be in comfort zone, background region recessed in screen and be in comfort zone；9th Kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and be in comfort zone, background Region convex in screen and be in comfort zone；10th kind of scene mode is the prospect in the right viewpoint parallax gray level image of stereo-picture Target recessed in screen and be in comfort zone, background region recessed in screen and be in comfort zone；

2. every width stereo-picture is selected to have a stereoscopic image data storehouse of the mean subjective scoring average of visual comfort； Then the parallax according to the foreground target in the right viewpoint parallax gray level image of the every width stereo-picture in stereoscopic image data storehouse Angle and the parallactic angle of background region, determine the scene mode belonging to every width stereo-picture in stereoscopic image data storehouse；Further according to Before belonging in stereoscopic image data storehouse in the respective right viewpoint parallax gray level image of all stereo-pictures of every kind of scene mode The parallactic angle of scape target and the parallactic angle of background region, set up the visual comfort evaluation model under every kind of scene mode, by n-th The visual comfort evaluation model planted under scene mode is described as follows: smmo_n=(4.2028-u (q_n))-v(q_n)×d_a(q_n)+ 0.1912×ln(w_a)-0.0208×d_a(q_n)×ln(w_a), wherein, 1≤n≤10, smmo_nRepresent under n scene mode The output of visual comfort evaluation model, u (q_n) and v (q_n) it is constant, d_a(q_n) represent that a width to be entered belongs to n The global disparity angle of the right viewpoint parallax gray level image of the stereo-picture to be evaluated of scene mode, d_a(q_n)=q_n×|f_a|+ (1-q_n)×|b_a|, q_nRepresent the weight under n scene mode, f_aThe width representing to be entered belongs to n scene mode The parallactic angle of the foreground target in the right viewpoint parallax gray level image of stereo-picture to be evaluated, b_aThe width representing to be entered belongs to The parallactic angle of the background region in the right viewpoint parallax gray level image of the stereo-picture to be evaluated of n scene mode, w_a Before in the right viewpoint parallax gray level image of stereo-picture to be evaluated that the width representing to be entered belongs to n scene mode The width angle of scape target, symbol " | | " it is the symbol that takes absolute value；

3. the right viewpoint parallax gray level image of stereo-picture to be evaluated is designated as { d_r(x, y) }, wherein, 1≤x≤w, 1 ≤ y≤h, w represent the width of stereo-picture to be evaluated, its with stereoscopic image data storehouse in every width stereo-picture width one Cause, h represents the height of stereo-picture to be evaluated, its with stereoscopic image data storehouse in every width stereo-picture highly consistent, d_r(x, y) represents { d_r(x, y) } in coordinate position be (x, y) pixel pixel value；Then adopt and step 2. middle identical Mode, determines the scene mode belonging to stereo-picture to be evaluated；Further according to the scene mode belonging to stereo-picture to be evaluated, Choose the visual comfort evaluation model under this scene mode；Then according to the visual comfort evaluation model under this scene mode And { d_r(x, y) } in the parallactic angle of foreground target, { d_r(x, y) } in the parallactic angle of background region and { d_r(x, y) } in before The width angle of scape target, calculates the visual comfort evaluation and foreca value of stereo-picture to be evaluated, is designated as smmo it is assumed that to be evaluated Stereo-picture belong to n scene mode, then smmo=(4.2028-u (q_n))-v(q_n)×d_a'(q_n)+0.1912×ln (w_a')-0.0208×d_a'(q_n)×ln(w_a'), wherein, d_a'(q_n) represent { d_r(x, y) } global disparity angle, d_a'(q_n)=q_n ×|f_a'|+(1-q_n)×|b_a' |, f_a' represent { d_r(x, y) } in foreground target parallactic angle, b_a' represent { d_r(x, y) } in The parallactic angle of background region, w_a' represent { d_r(x, y) } in foreground target width angle；

4. visual comfort evaluation and foreca value smmo of stereo-picture to be evaluated is modified, by solid to be evaluated The revised visual comfort evaluation and foreca value of image is designated as smm,Wherein, max () is to take max function, p The tortuosity attenuation coefficient of the visual comfort evaluation model under the expression scene mode belonging to stereo-picture to be evaluated, s_r' Represent { d_r(x, y) } in foreground target average prospect line hop count, s_c' represent { d_r(x, y) } in foreground target average Prospect alignment hop count, t_fRepresent the parallactic angle threshold value setting, t_rRepresent the prospect line hop count threshold value setting, t_cRepresent setting Prospect alignment hop count threshold value.

Described step 2. middle q_nAcquisition process be:

2. -1, assume that the total width number belonging to the stereo-picture of n scene mode in stereoscopic image data storehouse is m', its In, m' >=1；

2. -2, make q_nInitial value be 0.1, make q_nInitial value be 0；

2. -3, calculating the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene The global disparity angle of the right viewpoint parallax gray level image of every width stereo-picture of pattern, the weight under n scene mode is taken It is worth for q_nWhen stereoscopic image data storehouse in belong to n scene mode m' width stereo-picture right viewpoint parallax gray level image Global disparity angle be designated as d_m',a(q_n), d_m',a(q_n)=q_n×|f_m',a|+(1-q_n)×|b_m',a|, wherein, 1≤m'≤m', f_m',a And b_m',aThe corresponding right viewpoint parallax ash representing the m' width stereo-picture belonging to n scene mode in stereoscopic image data storehouse The parallactic angle of foreground target in degree image and the parallactic angle of background region, symbol " | | " it is the symbol that takes absolute value；

2. -4, calculating the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene Every width stereo-picture of pattern eliminates the comfort level objective evaluation value of parallax linear effect, by the power under n scene mode Refetching value is q_nWhen stereoscopic image data storehouse in belong to the m' width stereo-picture of n scene mode and eliminate the linear shadow of parallax The comfort level objective evaluation value rung is designated as vc_h(d_m',a(q_n),w_a), vc_h(d_m',a(q_n),w_a)=4.2028+0.1912 × ln (w_a)-0.0208×d_m',a(q_n)×ln(w_a)；Then calculating the weight value under n scene mode is q_nWhen stereo-picture number According to the comfort level objective evaluation value of the parallax linear effect of the every width stereo-picture belonging to n scene mode in storehouse, by n Weight value under scene mode is q_nWhen stereoscopic image data storehouse in belong to the m' width stereo-picture of n scene mode The comfort level objective evaluation value of parallax linear effect is designated as err_m', err_m'=vc_h(d_m',a(q_n),w_a)-mos_m', wherein, mos_m' Represent that the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to the m' width of n scene mode The comfort level subjective assessment value of stereo-picture；Adopt least square method again, be q to the weight value under n scene mode_nWhen The overall situation of the respective right viewpoint parallax gray level image of all stereo-pictures of n scene mode is belonged in stereoscopic image data storehouse The comfort level objective evaluation value of parallactic angle and respective parallax linear effect carries out linear fit, obtains under n scene mode Weight value be q_nWhen stereoscopic image data storehouse in belong to a matching corresponding to all stereo-pictures of n scene mode Linear equation: err=u (q_n)+v(q_n)×d_a(q_n), wherein, err is the linear effect for representing parallax to objective comfort level Dependent variable, u (q_n) and v (q_n) it is err=u (q_n)+v(q_n)×d_a(q_n) in constant, d_a(q_n) it is for representing n Weight value under scene mode is q_nWhen stereoscopic image data storehouse in belong to the arbitrary width stereo-picture of n scene mode The independent variable at the global disparity angle of right viewpoint parallax gray level image；

2. it is -5, q according to the weight value under n scene mode_nWhen stereoscopic image data storehouse in belong to n scene One fitting a straight line equation corresponding to all stereo-pictures of pattern, obtaining the weight value under n scene mode is q_nWhen The comfort level objective evaluation equation of all stereo-pictures of n scene mode: smmo is belonged in stereoscopic image data storehouse_n(q_n) =vc_h(d_a(q_n),w_a)-err=(4.2028-u (q_n))-v(q_n)×d_a(q_n)+0.1912×ln(w_a)-0.0208×d_a(q_n) ×ln(w_a), wherein, smmo_n(q_n) it is the comfort level objective evaluation value being superimposed parallax linear effect and parallax non-linear effects, vc_h(d_a(q_n),w_a) represent that the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene Arbitrary width stereo-picture of pattern eliminates the comfort level objective evaluation value of parallax linear effect；Then measure n scene mould Weight value under formula is q_nWhen stereoscopic image data storehouse in belong to n scene mode all stereo-pictures comfort level visitor See the fitting degree of evaluation of estimate and corresponding comfort level subjective assessment value；

2. -6, make q_n=q_n+ 0.1, it is then back to step and 2. -3 continue executing with, until q_nTerminate during equal to 1.1, obtain n-th Plant when the weight value under scene mode is respectively 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0 and intend accordingly Then corresponding for best fit degree weight value is assigned to q by conjunction degree_n, wherein, q_n=q_nIn+0.1 "=" it is assignment Symbol；

Described step 2. middle u (q_n) value be best fit degree corresponding fitting a straight line equation in a term system Number；Described step 2. middle v (q_n) value be best fit degree corresponding fitting a straight line equation in constant.

Described step is 2. in the right viewpoint parallax gray level image of every width stereo-picture in neutral body image data base The acquisition modes of foreground target and background region and described step 3. in stereo-picture to be evaluated right viewpoint parallax gray scale Foreground target in image is identical with the acquisition modes of background region, by the every width stereo-picture in stereoscopic image data storehouse and treating Evaluate stereo-picture all as pending stereo-picture, then in the right viewpoint parallax gray level image of pending stereo-picture before The acquisition process of scape target and background region is: obtains the intensity histogram of the right viewpoint parallax gray level image of pending stereo-picture Figure；Then maximum variance between clusters are adopted, and the intensity histogram of the right viewpoint parallax gray level image according to pending stereo-picture Figure, obtains the intensity slicing threshold value of the right viewpoint parallax gray level image of pending stereo-picture, is designated as p_seg；Again will be pending vertical In the right viewpoint parallax gray level image of body image, pixel value is more than or equal to p_segPixel be defined as foreground pixel point, and will In the right viewpoint parallax gray level image of pending stereo-picture, pixel value is less than p_segPixel be defined as background pixel；? It is made up of the foreground target in the right viewpoint parallax gray level image of pending stereo-picture afterwards all foreground pixel points, after all Scene vegetarian refreshments constitutes the background region in the right viewpoint parallax gray level image of pending stereo-picture.

Intensity slicing threshold value p of the right viewpoint parallax gray level image of described pending stereo-picture_segAcquisition process For:

X1, set up intensity slicing threshold value find object function, be designated as t, t=w_f×(μ-μ_f)²+w_b×(μ-μ_b)², wherein, w_fRepresent that the total number of the foreground pixel point in the right viewpoint parallax gray level image of pending stereo-picture accounts for pending stereogram The ratio of the total number of pixel in the right viewpoint parallax gray level image of picture,μ_fRepresent pending vertical The average of the pixel value of all foreground pixel points in the right viewpoint parallax gray level image of body image,w_bRepresent background pixel in the right viewpoint parallax gray level image of pending stereo-picture Total number accounts for the ratio of the total number of pixel in the right viewpoint parallax gray level image of pending stereo-picture,μ_bRepresent all background pixels in the right viewpoint parallax gray level image of pending stereo-picture The average of pixel value,μ represents in the right viewpoint parallax gray level image of pending stereo-picture The average of the pixel value of all pixels point, μ=w_f×μ_f+w_b×μ_b, hist_dG () represents that the right viewpoint of pending stereo-picture regards Grey level histogram { the hist of difference gray level image_d(g) } in pixel value be g pixel number, 0≤g_min≤g≤g_max≤ 255, g_minRepresent the grey level histogram { hist of the right viewpoint parallax gray level image of pending stereo-picture_d(g) } in pixel The minimum pixel value that number is more than 0, g_maxRepresent the grey level histogram of the right viewpoint parallax gray level image of pending stereo-picture {hist_d(g) } in pixel number be more than 0 max pixel value；

X2, in interval [g_min,g_max] interior traversal t, the value of t when making t maximum is defined as pending stereo-picture Intensity slicing threshold value p of right viewpoint parallax gray level image_seg.

The described step 2. middle mode determining the scene mode belonging to every width stereo-picture in stereoscopic image data storehouse Identical with the mode of the scene mode belonging to described step 3. middle determination stereo-picture to be evaluated, by stereoscopic image data storehouse In every width stereo-picture and stereo-picture to be evaluated all as pending stereo-picture it is determined that pending stereo-picture institute The detailed process of the scene mode belonging to is:

Z1, for the foreground target in the right viewpoint parallax gray level image of pending stereo-picture, if f_a ^*> 1 ° it is determined that Foreground target convex in screen and be in non-comfort zone；If 0 °≤f_a ^*< 1 ° it is determined that foreground target convex in screen and be in comfortable Area；If -1 ° of < f_a ^*< 0 ° it is determined that foreground target recessed in screen and be in comfort zone；If f_a ^*< -1 ° it is determined that foreground target is recessed In screen and be in non-comfort zone；

For the background region in the right viewpoint parallax gray level image of pending stereo-picture, if b_a ^*> 1 ° it is determined that background Region convex in screen and be in non-comfort zone；If 0 °≤b_a ^*< 1 ° it is determined that background region convex in screen and be in comfort zone；If- 1 ° of ＜ b_a ^*< 0 ° it is determined that background region recessed in screen and be in comfort zone；If b_a ^*< -1 ° it is determined that background region recessed in screen And it is in non-comfort zone；

Wherein, f_a ^*Represent the parallactic angle of the foreground target in the right viewpoint parallax gray level image of pending stereo-picture, f_a ^* =f-k, b_a ^*Represent the parallactic angle of the background region in the right viewpoint parallax gray level image of pending stereo-picture, b_a ^*=b-k, f Represent that human eye binocular watches the convergent angle of foreground target,After b represents the viewing of human eye binocular The convergent angle of scene area,K represents the adjustment angle of human eye binocular,Arctan () is tan of negating, and p represents the interpupillary distance of human eye binocular, and l represents the width of display Degree, n represents the horizontal resolution of display, and h represents human eye to the distance of display, and f represents that the right side of pending stereo-picture regards Point parallax gray level image in foreground target mean parallax amplitude, f in units of pixel, $f=\frac{\underset{x=1}{\overset{w}{\&sigma;}}\underset{y=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}\left({d_r}^{*}\right(x,y)-p_{seg})\&times;{d_r}^{*}(x,y)}{\underset{x=1}{\overset{w}{\&sigma;}}\underset{y=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}\left({d_r}^{*}\right(x,y)-p_{seg})},\mathrm{\&epsiv;}\left({d_r}^{*}\right(x,y)-p_{seg})=\left\{\begin{array}{c}1,{d_r}^{*}(x,y)-p_{seg}\&greaterequal;0\\ 0,{d_r}^{*}(x,y)-p_{seg}<0\end{array}\right.,$ B represents and treats Process stereo-picture right viewpoint parallax gray level image in background region mean parallax amplitude, b in units of pixel, $b=\frac{\underset{x=1}{\overset{w}{\&sigma;}}\underset{y=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}(p_{seg}-{d_r}^{*}(x,y\left)\right)\&times;{d_r}^{*}(x,y)}{\underset{x=1}{\overset{w}{\&sigma;}}\underset{y=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}(p_{seg}-{d_r}^{*}(x,y\left)\right)},\mathrm{\&epsiv;}(p_{seg}-{d_r}^{*}(x,y\left)\right)=\left\{\begin{array}{c}1,p_{seg}-{d_r}^{*}(x,y)\&greaterequal;0\\ 0,p_{seg}-{d_r}^{*}(x,y)<0\end{array}\right.,$ d_r ^*(x,y) Represent that in the right viewpoint parallax gray level image of pending stereo-picture, coordinate position is the pixel value of the pixel of (x, y)；

Z2, concavo-convex with respect to screen according to the foreground target in the right viewpoint parallax gray level image of pending stereo-picture Property and whether be in comfort zone, whether background region with respect to the concavity and convexity of screen and is in comfort zone, determines pending Scene mode belonging to stereo-picture, if particularly as follows: foreground target convex in screen and be in non-comfort zone, background region recessed in screen Curtain and be in non-comfort zone it is determined that pending stereo-picture belongs to the 1st kind of scene mode；If foreground target convex in screen and place In non-comfort zone, background region convex in screen and be in non-comfort zone it is determined that pending stereo-picture belongs to the 2nd kind of scene mould Formula；If foreground target recessed in screen and be in non-comfort zone, background region recessed in screen and be in non-comfort zone it is determined that waiting to locate Reason stereo-picture belongs to the 3rd kind of scene mode；If foreground target convex in screen and be in non-comfort zone, background region recessed in screen And it is in comfort zone it is determined that pending stereo-picture belongs to the 4th kind of scene mode；If foreground target convex in screen and be in non- Comfort zone, background region convex in screen and be in comfort zone it is determined that pending stereo-picture belongs to the 5th kind of scene mode；If Foreground target convex in screen and be in comfort zone, background region recessed in screen and be in non-comfort zone it is determined that pending solid Image belongs to the 6th kind of scene mode；If foreground target recessed in screen and be in comfort zone, background region recessed in screen and be in non- Comfort zone is it is determined that pending stereo-picture belongs to the 7th kind of scene mode；If foreground target convex in screen and be in comfort zone, Background region recessed in screen and be in comfort zone it is determined that pending stereo-picture belongs to the 8th kind of scene mode；If foreground target Convex in screen and be in comfort zone, background region convex in screen and be in comfort zone it is determined that pending stereo-picture belongs to the 9th Plant scene mode；If foreground target recessed in screen and be in comfort zone, background region recessed in screen and be in comfort zone it is determined that Pending stereo-picture belongs to the 10th kind of scene mode.

Described step 3. in { d_r(x, y) } in foreground target width angleWherein, Arctan () is to negate tan, and h represents human eye to the distance of display, w_frontRepresent { d_r(x, y) } in foreground target Mean breadth, w_frontAcquisition process be:

A1, to { d_r(x, y) } carry out binary conversion treatment, obtain { d_r(x, y) } binary image, be designated as { bi (x, y) }, The pixel value of the pixel that coordinate position in { bi (x, y) } is (x, y) is designated as bi (x, y), as { d_r(x, y) } in coordinate position When belonging to foreground target for the pixel of (x, y), make bi (x, y)=1, as { d_r(x, y) } in coordinate position be (x, y) pixel When point belongs to background region, make bi (x, y)=0；

A2, line scans are entered to { bi (x, y) }, for the row row in { bi (x, y) }, from the 1st picture of row row Vegetarian refreshments starts to scan to the right, when scanning the pixel that the 1st pixel value is 1, using this pixel as the 1st section of prospect width The original position of line segment, and the row coordinate of this pixel is designated as x₁, continue scanning to the right until scanning the picture that pixel value is 0 The 1st section of prospect width line segment is obtained, the row coordinate of the pixel that this pixel value is 0 is designated as x till vegetarian refreshments₂, by the 1st section of prospect The length of width line segment is designated as wl₁, wl₁=x₂-x₁；Continue scan to the right, with obtain the 1st section of prospect width line segment identical Mode, obtains all prospect width line segments in row row；Wherein, the initial value of row is 1,1≤row≤h, 1≤x₁<x₂≤ w；

A3, length in { bi (x, y) } is less than all prospect width line segments of 0.002w and length is more than the institute of 0.995w Prospect width line segment is had to remove；Then by length, order from small to large is ranked up to remaining all prospect width line segments, The prospect width line segment taking middle 80% constitutes prospect width line segment aggregate, is designated as { wl'_n, wherein, wl'_nRepresent { wl'_nIn N-th section of prospect width line segment length, 1≤n≤n', n' represent { wl'_nIn total hop count of prospect width line segment of comprising；

A4, calculating { wl'_nIn the length of all prospect width line segments mean value, be designated as w_front',Then by w_front' as { d_r(x, y) } in foreground target mean breadth w_frontEven, w_front= w_front'.

Described step 4. in { d_r(x, y) } in foreground target average prospect line hop count {d_r(x, y) } in foreground target average prospect alignment hop countWherein, 1≤row≤h, 1≤col ≤ w, rlq_rowRepresent { d_r(x, y) } in row row in prospect line section quantity, $\mathrm{\&epsiv;}\left({\mathrm{rlq}}_{row}\right)=\{\begin{array}{c}1,{\mathrm{rlq}}_{row}>0\\ 0,{\mathrm{rlq}}_{row}\&le;0\end{array},$ clq_colRepresent { d_r(x, y) } in col row in prospect alignment section quantity, $\mathrm{\&epsiv;}\left({\mathrm{clq}}_{col}\right)=\left\{\begin{array}{c}1,cl{q}_{col}>0\\ 0,cl{q}_{col}\&le;0\end{array}\right.;$

Wherein, rlq_rowAnd clq_colAcquisition process be:

B1, to { d_r(x, y) } carry out binary conversion treatment, obtain { d_r(x, y) } binary image, be designated as { bi (x, y) }, The pixel value of the pixel that coordinate position in { bi (x, y) } is (x, y) is designated as bi (x, y), as { d_r(x, y) } in coordinate position When belonging to foreground target for the pixel of (x, y), make bi (x, y)=1, as { d_r(x, y) } in coordinate position be (x, y) pixel When point belongs to background region, make bi (x, y)=0；

B2,2 dilation operations that { bi (x, y) } is carried out successively with mathematical morphology, 4 erosion operations, 2 expansion fortune Calculate, obtain { bi'(x, y) }, wherein, bi'(x, y) represent that in { bi'(x, y) }, coordinate position is the pixel of the pixel of (x, y) Value；

The quantity of effective prospect line section in often going in b3, statistics { bi'(x, y) }, by the in { bi'(x, y) } In row row, the quantity of effective prospect line section is designated as rlq_row', rlq_row' acquisition process be: b3-1, make rlq_row' just Initial value is 0；B3-2, the 1st pixel of row row from { bi'(x, y) } start to scan to the right, are scanning the 1st picture During the pixel for 1 for the element value, using this pixel as the original position of the 1st section of prospect line section, and the row of this pixel are sat It is labeled as x₁', continue scanning to the right and obtain the 1st section of prospect line section till scanning the pixel that pixel value is 0, should Pixel value is that the row coordinate of 0 pixel is designated as x₂', if length x of the 1st section of prospect line section₂'-x₁' it is more than 0.005w, then really Fixed 1st section of prospect line section is effective prospect line section, and makes rlq_row'=rlq_row'+1, if the 1st section of prospect line section Length x₂'-x₁' it is less than or equal to 0.005w it is determined that the 1st section of prospect line section is invalid prospect line section；Continue to sweep to the right Retouch, with obtain the 1st section of prospect line section determine whether the 1st section of prospect line section is effective prospect line section identical side Formula, obtains all effective prospect line section in row row, and counts and obtain effective prospect line section in row row Quantity rlq_row'；Wherein, the initial value of row is 1,1≤row≤h, 1≤x₁'<x₂'≤w, rlq_row'=rlq_rowIn '+1 "=" is assignment；

Equally, in often going in statistics { bi'(x, y) } effective prospect alignment section quantity, by { bi'(x, y) } In col row, the quantity of effective prospect alignment section is designated as clq_col', clq_col' acquisition process be: b3-1), make clq_col' Initial value is 0；B3-2), the 1st pixel of the col row from { bi'(x, y) } starts to scan downwards, is scanning the 1st When individual pixel value is 1 pixel, using this pixel as the 1st section of prospect alignment section original position, and by this pixel Row coordinate is designated as y₁', continue scanning downwards and obtain the 1st section of prospect alignment section till scanning the pixel that pixel value is 0, The row coordinate of the pixel that this pixel value is 0 is designated as y₂', if length y of the 1st section of prospect alignment section₂'-y₁' it is more than 0.005h, Then determine that the 1st section of prospect alignment section is effective prospect alignment section, and make clq_col'=clq_col'+1, if the 1st section of prostatitis alignment Length y of section₂'-y₁' it is less than or equal to 0.005h it is determined that the 1st section of prospect alignment section is invalid prospect alignment section；Continue to Lower scanning, with obtain the 1st section of prospect alignment section determine whether the 1st section of prospect alignment section is that effective prospect alignment section is identical Mode, obtain all effective prospect alignment section in col row, and count and obtain in col row effectively prospect alignment Quantity clq of section_col'；Wherein, the initial value of col is 1,1≤col≤w, 1≤y₁'<y₂'≤h, clq_col'=clq_colIn '+1 "=" it is assignment；

B4, make rlq_row=rlq_row', make clq_col=clq_col'.

Described step 4. in take p=1.6, t_f=2.0, t_r=2, t_c=1.5.

Compared with prior art, it is an advantage of the current invention that: nature according to three-dimensional display for the inventive method In scene, it is determined that 10 kinds of scene modes of stereo-picture, this can be abundant for foreground target and the depth location residing for background region Embody the non-linear relation of the depth factor of object and visual comfort in stereo-picture, thus based on 10 kinds of scene modes respectively The visual comfort evaluation model set up can more accurately reflect the sense of the visual comfort to stereo-picture for the human visual system Know such that it is able to effectively improve the uniformity between objective evaluation result and human eye subjective perception.

Brief description

Fig. 1 totally realizes block diagram for the inventive method；

Fig. 2 is the geometric representation of f, b, k, p, h, f, b；

Fig. 3 is w '_aGeometric representation；

Fig. 4 a is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.1 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 b is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.2 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 c is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.3 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 d is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.4 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 e is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.5 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 f is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.6 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 g is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.7 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 h is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.8 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 i is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 0.9 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 4 j is the weight value under the 4th kind of scene mode is to belong to the 4th kind of scene mould in stereoscopic image data storehouse when 1.0 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 5 is to belong to the 4th in stereoscopic image data storehouse during weight value respectively 0.1～1.0 under the 4th kind of scene mode (fitting degree is weighed with mae, and weight value is for mae when 0.7 to plant fitting degree corresponding to all stereo-pictures of scene mode Little, that is, best fit degree corresponding weight value is 0.7)；

Fig. 6 a is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.1 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 b is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.2 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 c is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.3 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 d is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.4 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 e is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.5 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 f is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.6 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 g is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.7 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 h is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.8 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 i is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 0.9 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 6 j is the weight value under the 5th kind of scene mode is to belong to the 5th kind of scene mould in stereoscopic image data storehouse when 1.0 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 7 is to belong to the 5th in stereoscopic image data storehouse during weight value respectively 0.1～1.0 under the 5th kind of scene mode (fitting degree is weighed with mae, and weight value is for mae when 0.6 to plant fitting degree corresponding to all stereo-pictures of scene mode Little, that is, best fit degree corresponding weight value is 0.6)；

Fig. 8 a is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.1 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 b is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.2 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 c is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.3 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 d is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.4 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 e is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.5 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 f is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.6 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 g is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.7 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 h is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.8 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 i is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 0.9 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 8 j is the weight value under the 8th kind of scene mode is to belong to the 8th kind of scene mould in stereoscopic image data storehouse when 1.0 Linear Fit Chart corresponding to all stereo-pictures of formula, abscissa is global disparity angle, and ordinate is the visitor of parallax linear effect See comfortable angle value；

Fig. 9 is to belong to the 8th in stereoscopic image data storehouse during weight value respectively 0.1～1.0 under the 8th kind of scene mode (fitting degree is weighed with mae, and weight value is for mae when 0.6 to plant fitting degree corresponding to all stereo-pictures of scene mode Little, that is, best fit degree corresponding weight value is 0.6)；

Figure 10 a is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.1 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 b is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.2 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 c is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.3 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 d is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.4 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 e is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.5 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 f is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.6 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 g is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.7 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 h is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.8 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 i is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 0.9 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 10 j is the weight value under the 9th kind of scene mode is to belong to the 9th kind of scene in stereoscopic image data storehouse when 1.0 Linear Fit Chart corresponding to all stereo-pictures of pattern, abscissa is global disparity angle, and ordinate is parallax linear effect Objective comfortable angle value；

Figure 11 is to belong to the in stereoscopic image data storehouse when weight value under the 9th kind of scene mode is respectively 0.1～1.0 Fitting degree corresponding to all stereo-pictures of 9 kinds of scene modes (weighed with mae, and weight value is mae when 0.5 by fitting degree Minimum, that is, best fit degree corresponding weight value is 0.5).

Specific embodiment

Below in conjunction with accompanying drawing embodiment, the present invention is described in further detail.

A kind of stereo image vision comfort level evaluation method based on scene mode classification proposed by the present invention, it is totally real Existing block diagram is as shown in figure 1, it comprises the following steps:

1. 10 kinds of scene modes of the natural scene shown by three-dimensional display are determined, the 1st kind of scene mode is stereogram Foreground target in the right viewpoint parallax gray level image of picture convex in screen and be in non-comfort zone, background region recessed in screen and place In non-comfort zone；2nd kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and Be in non-comfort zone, background region convex in screen and be in non-comfort zone；3rd kind of scene mode is that the right viewpoint of stereo-picture regards Foreground target in difference gray level image recessed in screen and be in non-comfort zone, background region recessed in screen and be in non-comfort zone； 4th kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and be in non-comfortable Area, background region recessed in screen and be in comfort zone；5th kind of scene mode is in the right viewpoint parallax gray level image of stereo-picture Foreground target convex in screen and be in non-comfort zone, background region convex in screen and be in comfort zone；6th kind of scene mode be Foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and be in comfort zone, background region recessed in screen And it is in non-comfort zone；7th kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture recessed in screen Curtain and be in comfort zone, background region recessed in screen and be in non-comfort zone；8th kind of scene mode is the right viewpoint of stereo-picture Foreground target in parallax gray level image convex in screen and be in comfort zone, background region recessed in screen and be in comfort zone；9th Kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and be in comfort zone, background Region convex in screen and be in comfort zone；10th kind of scene mode is the prospect in the right viewpoint parallax gray level image of stereo-picture Target recessed in screen and be in comfort zone, background region recessed in screen and be in comfort zone.

2. every width stereo-picture is selected to have a stereoscopic image data storehouse of the mean subjective scoring average of visual comfort； Then the parallax according to the foreground target in the right viewpoint parallax gray level image of the every width stereo-picture in stereoscopic image data storehouse Angle and the parallactic angle of background region, determine the scene mode belonging to every width stereo-picture in stereoscopic image data storehouse；Further according to Before belonging in stereoscopic image data storehouse in the respective right viewpoint parallax gray level image of all stereo-pictures of every kind of scene mode The parallactic angle of scape target and the parallactic angle of background region, set up the visual comfort evaluation model under every kind of scene mode, by n-th The visual comfort evaluation model planted under scene mode is described as follows: smmo_n=(4.2028-u (q_n))-v(q_n)×d_a(q_n)+ 0.1912×ln(w_a)-0.0208×d_a(q_n)×ln(w_a), wherein, 1≤n≤10, smmo_nRepresent under n scene mode The output of visual comfort evaluation model, u (q_n) and v (q_n) it is constant, u (q_n) and v (q_n) value by the u in step 2. -4 (q_n) and v (q_n) determine, d_a(q_n) represent that a width to be entered belongs to the right side of the stereo-picture to be evaluated of n scene mode The global disparity angle of viewpoint parallax gray level image, d_a(q_n)=q_n×|f_a|+(1-q_n)×|b_a|, q_nRepresent n scene mode Under weight, f_aThe width representing to be entered belongs to the right viewpoint parallax ash of the stereo-picture to be evaluated of n scene mode The parallactic angle of the foreground target in degree image, b_aThe width representing to be entered belongs to the solid to be evaluated of n scene mode The parallactic angle of the background region in the right viewpoint parallax gray level image of image, w_aThe width representing to be entered belongs to n scene The width angle of the foreground target in the right viewpoint parallax gray level image of the stereo-picture to be evaluated of pattern, symbol " | | " for taking absolutely To value symbol.

In this particular embodiment, step 2. middle q_nAcquisition process be:

2. -1, assume that the total width number belonging to the stereo-picture of n scene mode in stereoscopic image data storehouse is m', its In, m' >=1.

2. -2, make q_nInitial value be 0.1, make q_nInitial value be 0.

2. -3, calculating the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene The global disparity angle of the right viewpoint parallax gray level image of every width stereo-picture of pattern, the weight under n scene mode is taken It is worth for q_nWhen stereoscopic image data storehouse in belong to n scene mode m' width stereo-picture right viewpoint parallax gray level image Global disparity angle be designated as d_m',a(q_n), d_m',a(q_n)=q_n×|f_m',a|+(1-q_n)×|b_m',a|, wherein, 1≤m'≤m', f_m',a And b_m',aThe corresponding right viewpoint parallax ash representing the m' width stereo-picture belonging to n scene mode in stereoscopic image data storehouse The parallactic angle of foreground target in degree image and the parallactic angle of background region, symbol " | | " it is the symbol that takes absolute value.

2. -4, calculating the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene Every width stereo-picture of pattern eliminates the comfort level objective evaluation value of parallax linear effect, by the power under n scene mode Refetching value is q_nWhen stereoscopic image data storehouse in belong to the m' width stereo-picture of n scene mode and eliminate the linear shadow of parallax The comfort level objective evaluation value rung is designated as vc_h(d_m',a(q_n),w_a), vc_h(d_m',a(q_n),w_a)=4.2028+0.1912 × ln (w_a)-0.0208×d_m',a(q_n)×ln(w_a)；Then calculating the weight value under n scene mode is q_nWhen stereo-picture number According to the comfort level objective evaluation value of the parallax linear effect of the every width stereo-picture belonging to n scene mode in storehouse, by n Weight value under scene mode is q_nWhen stereoscopic image data storehouse in belong to the m' width stereo-picture of n scene mode The comfort level objective evaluation value of parallax linear effect is designated as err_m', err_m'=vc_h(d_m',a(q_n),w_a)-mos_m', wherein, mos_m' Represent that the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to the m' width of n scene mode The comfort level subjective assessment value of stereo-picture, mos_m'Value known to；Adopt existing least square method again, to n scene mould Weight value under formula is q_nWhen stereoscopic image data storehouse in belong to the respective right side of all stereo-pictures of n scene mode and regard The comfort level objective evaluation value of the global disparity angle of point parallax gray level image and respective parallax linear effect carries out linear fit, Obtaining the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to all vertical of n scene mode The corresponding fitting a straight line equation of body image: err=u (q_n)+v(q_n)×d_a(q_n), wherein, err is for representing parallax pair The dependent variable of the linear effect of objective comfort level, u (q_n) and v (q_n) it is err=u (q_n)+v(q_n)×d_a(q_n) in constant, u (q_n) and v (q_n) value be matching result out, d_a(q_n) it is for representing that the weight value under n scene mode is q_nWhen The overall situation belonging to the right viewpoint parallax gray level image of arbitrary width stereo-picture of n scene mode in stereoscopic image data storehouse regards The independent variable of declinate.

2. it is -5, q according to the weight value under n scene mode_nWhen stereoscopic image data storehouse in belong to n scene One fitting a straight line equation corresponding to all stereo-pictures of pattern, obtaining the weight value under n scene mode is q_nWhen The comfort level objective evaluation equation of all stereo-pictures of n scene mode: smmo is belonged in stereoscopic image data storehouse_n(q_n) =vc_h(d_a(q_n),w_a)-err=(4.2028-u (q_n))-v(q_n)×d_a(q_n)+0.1912×ln(w_a)-0.0208×d_a(q_n) ×ln(w_a), wherein, smmo_n(q_n) it is the comfort level objective evaluation value being superimposed parallax linear effect and parallax non-linear effects, vc_h(d_a(q_n),w_a) represent that the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene Arbitrary width stereo-picture of pattern eliminates the comfort level objective evaluation value of parallax linear effect；Then adopt existing averagely exhausted Value error (mae) is measured with the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene The fitting degree of the comfort level objective evaluation value of all stereo-pictures of pattern and corresponding comfort level subjective assessment value.

2. -6, make q_n=q_n+ 0.1, it is then back to step and 2. -3 continue executing with, until q_nTerminate during equal to 1.1, obtain n-th Plant when the weight value under scene mode is respectively 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0 and intend accordingly Then corresponding for best fit degree weight value is assigned to q by conjunction degree_n, wherein, q_n=q_nIn+0.1 "=" it is assignment Symbol.

In this particular embodiment, step 2. middle u (q_n) value be best fit degree corresponding fitting a straight line equation In Monomial coefficient, v (q_n) value be best fit degree corresponding fitting a straight line equation in constant.

Here, the stereoscopic image data being provided with Korea Advanced Institute of Science and Technology image and video system laboratory (ivy lab) As a example storehouse, this stereoscopic image data storehouse comprises 120 width stereo-pictures and corresponding right viewpoint parallax gray level image, this stereogram As database contains the indoor and outdoors stereo-picture of various scene depths, and give the euphorosia of every width stereo-picture The mean subjective scoring average of degree；Calculate the right viewpoint parallax gray level image of the every width stereo-picture in this stereoscopic image data storehouse In the parallactic angle of foreground target and background region parallactic angle, obtain foreground target and background region each with respect to screen Concavity and convexity and the characteristic whether being located at euphorosia area, it is possible to find the stereo-picture in this stereoscopic image data storehouse is only limitted to 4th kind, the 5th kind, the 8th kind and the 9th kind of scene mode, therefore build taking the 4th kind, the 5th kind, the 8th kind and the 9th kind of scene mode as a example Visual comfort evaluation model under vertical 4th kind, the 5th kind, the 8th kind and the 9th kind of scene mode.

Stereo-picture when the weight value that Fig. 4 a to Fig. 4 j correspondence gives under the 4th kind of scene mode is respectively 0.1～1.0 Linear Fit Chart corresponding to all stereo-pictures of the 4th kind of scene mode is belonged to, Fig. 5 gives the 4th kind of scene mould in database All stereo-pictures of the 4th kind of scene mode are belonged in stereoscopic image data storehouse when weight value under formula is respectively 0.1～1.0 Corresponding fitting degree (weighed with mae, and weight value is that when 0.7, mae is minimum, and that is, best fit degree is corresponding by fitting degree Weight value is 0.7)；When the weight value that Fig. 6 a to Fig. 6 j correspondence gives under the 5th kind of scene mode is respectively 0.1～1.0 Linear Fit Chart corresponding to all stereo-pictures of the 5th kind of scene mode is belonged to, Fig. 7 gives the 5th in stereoscopic image data storehouse Plant and in stereoscopic image data storehouse when the weight value under scene mode is respectively 0.1～1.0, belong to all of the 5th kind of scene mode Fitting degree corresponding to stereo-picture (weighed with mae, and weight value is that when 0.6, mae is minimum, i.e. best fit journey by fitting degree Spending corresponding weight value is 0.6)；The weight value that Fig. 8 a to Fig. 8 j correspondence gives under the 8th kind of scene mode is respectively 0.1 Linear Fit Chart corresponding to all stereo-pictures of the 8th kind of scene mode is belonged to, Fig. 9 gives in stereoscopic image data storehouse when～1.0 Go out and belonged to the 8th kind of scene mould in stereoscopic image data storehouse when the weight value under the 8th kind of scene mode is respectively 0.1～1.0 Fitting degree corresponding to all stereo-pictures of formula (weighed with mae, and weight value is that when 0.6, mae is minimum, that is, by fitting degree The corresponding weight value of good fitting degree is 0.6)；The weight that Figure 10 a to Figure 10 j correspondence gives under the 9th kind of scene mode takes Linear Quasi corresponding to all stereo-pictures of the 9th kind of scene mode is belonged in stereoscopic image data storehouse when value is respectively 0.1～1.0 Close figure, belong in stereoscopic image data storehouse when the weight value that Figure 11 gives under the 9th kind of scene mode is respectively 0.1～1.0 Fitting degree corresponding to all stereo-pictures of the 9th kind of scene mode (weighed with mae, when weight value is 0.5 by fitting degree Mae is minimum, and that is, best fit degree corresponding weight value is 0.5).

It is respectively as follows: q according to calculating the weight under the 4th kind, the 5th kind, the 8th kind and the 9th kind of scene mode above₄=0.7, q₅ =0.6, q₈=0.6, q₉=0.5, the visual comfort evaluation model under the 4th kind, the 5th kind, the 8th kind and the 9th kind of scene mode divides It is not:

smmo₄=4.3938-0.6652 × d_a(q₄)+0.1912×ln(w_a)-0.0208×d_a(q₄)×ln(w_a)

smmo₅=4.2326-0.7120 × d_a(q₅)+0.1912×ln(w_a)-0.0208×d_a(q₅)×ln(w_a)

smmo₈=4.5232-0.8918 × d_a(q₈)+0.1912×ln(w_a)-0.0208×d_a(q₈)×ln(w_a)

smmo₉=5.4614-2.6905 × d_a(q₉)+0.1912×ln(w_a)-0.0208×d_a(q₉)×ln(w_a)

3. the left view point coloured image of stereo-picture to be evaluated is designated as { i_l(x, y) }, by stereo-picture to be evaluated Right viewpoint coloured image be designated as { i_r(x, y) }, the right viewpoint parallax gray level image of stereo-picture to be evaluated is designated as { d_r(x, Y) }, wherein, 1≤x≤w, 1≤y≤h, w represent the width of stereo-picture to be evaluated, its with stereoscopic image data storehouse in every The width of width stereo-picture is consistent, and h represents the height of stereo-picture to be evaluated, and it is stood with the every width in stereoscopic image data storehouse Highly consistent, the i of body image_l(x, y) represents { i_l(x, y) } in coordinate position be (x, y) pixel pixel value, i_r(x,y) Represent { i_r(x, y) } in coordinate position be (x, y) pixel pixel value, d_r(x, y) represents { d_r(x, y) } in coordinate position Pixel value for the pixel of (x, y)；Then adopt and step 2. middle identical mode, determine belonging to stereo-picture to be evaluated Scene mode；Further according to the scene mode belonging to stereo-picture to be evaluated, choose the visual comfort under this scene mode Evaluation model；Then according to the visual comfort evaluation model under this scene mode and { d_r(x, y) } in the regarding of foreground target Declinate, { d_r(x, y) } in the parallactic angle of background region and { d_r(x, y) } in foreground target width angle, calculate to be evaluated The visual comfort evaluation and foreca value of stereo-picture, is designated as smmo it is assumed that stereo-picture to be evaluated belongs to n scene mould Formula, then smmo=(4.2028-u (q_n))-v(q_n)×d_a'(q_n)+0.1912×ln(w_a')-0.0208×d_a'(q_n)×ln (w_a'), wherein, d_a'(q_n) represent { d_r(x, y) } global disparity angle, d_a'(q_n)=q_n×|f_a'|+(1-q_n)×|b_a' |, f_a' Represent { d_r(x, y) } in foreground target parallactic angle, b_a' represent { d_r(x, y) } in background region parallactic angle, w_a' represent {d_r(x, y) } in foreground target width angle.

In this particular embodiment, step 3. in { d_r(x, y) } in foreground target width angleWherein, arctan () is to negate tan, and h represents human eye to the distance of display, w_front Represent { d_r(x, y) } in foreground target mean breadth, w_frontAcquisition process be:

A1, to { d_r(x, y) } carry out binary conversion treatment, obtain { d_r(x, y) } binary image, be designated as { bi (x, y) }, The pixel value of the pixel that coordinate position in { bi (x, y) } is (x, y) is designated as bi (x, y), as { d_r(x, y) } in coordinate position When belonging to foreground target for the pixel of (x, y), make bi (x, y)=1, as { d_r(x, y) } in coordinate position be (x, y) pixel When point belongs to background region, make bi (x, y)=0.

A2, line scans are entered to { bi (x, y) }, for the row row in { bi (x, y) }, from the 1st picture of row row Vegetarian refreshments starts to scan to the right, when scanning the pixel that the 1st pixel value is 1, using this pixel as the 1st section of prospect width The original position of line segment, and the row coordinate of this pixel is designated as x₁, continue scanning to the right until scanning the picture that pixel value is 0 The 1st section of prospect width line segment is obtained, the row coordinate of the pixel that this pixel value is 0 is designated as x till vegetarian refreshments₂, by the 1st section of prospect The length of width line segment is designated as wl₁, wl₁=x₂-x₁；Continue scan to the right, with obtain the 1st section of prospect width line segment identical Mode, obtains all prospect width line segments in row row；Wherein, the initial value of row is 1,1≤row≤h, 1≤x₁<x₂≤ w.

A3, remove the abnormal prospect width line segment of length in { bi (x, y) }, that is, remove especially short prospect width line segment and Especially long prospect width line segment, will length be less than all prospect width line segments of 0.002w and length is big in { bi (x, y) } All prospect width line segments in 0.995w remove；Then press length order from small to large to remaining all prospect wide lines Section is ranked up, and takes middle 80% prospect width line segment to constitute prospect width line segment aggregate, is designated as { wl'_n, wherein, wl'_nTable Show { wl'_nIn n-th section of prospect width line segment length, 1≤n≤n', n' represent { wl'_nIn the prospect width line segment that comprises Total hop count.

A4, calculating { wl'_nIn the length of all prospect width line segments mean value, be designated as w_front',Then by w_front' as { d_r(x, y) } in foreground target mean breadth w_frontEven, w_front= w_front'.

Fig. 3 gives w_a' geometric representation.

4. visual comfort evaluation and foreca value smmo of stereo-picture to be evaluated is modified, by stereo-picture to be evaluated Revised visual comfort evaluation and foreca value be designated as smm, Wherein, max () is to take max function, and p represents that the visual comfort under the scene mode belonging to stereo-picture to be evaluated is commented The tortuosity attenuation coefficient of valency model, s_r' represent { d_r(x, y) } in foreground target average prospect line hop count, s_c' represent {d_r(x, y) } in foreground target average prospect alignment hop count, t_fRepresent the parallactic angle threshold value setting, t_rRepresent before setting Scape line hop count threshold value, t_cRepresent the prospect alignment hop count threshold value setting, take p=1.6, t in the present embodiment_f=2.0, t_r= 2、t_c=1.5.

In this particular embodiment, step 4. in { d_r(x, y) } in foreground target average prospect line hop count ${s_r}^{\&prime;}=\frac{\underset{row=1}{\overset{h}{\&sigma;}}{\mathrm{rlq}}_{row}}{\underset{row=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}\left({\mathrm{rlq}}_{row}\right)},$ {d_r(x, y) } in foreground target average prospect alignment hop count ${s_c}^{\&prime;}=\frac{\underset{col=1}{\overset{w}{\&sigma;}}{\mathrm{clq}}_{col}}{\underset{col=1}{\overset{w}{\&sigma;}}\mathrm{\&epsiv;}\left({\mathrm{clq}}_{col}\right)},$ Its In, s_r' and s_c' it is { d_r(x, y) } in foreground target tortuosity parameter, 1≤row≤h, 1≤col≤w, rlq_rowRepresent {d_r(x, y) } in row row in prospect line section quantity, $\mathrm{\&epsiv;}\left({\mathrm{rlq}}_{row}\right)=\{\begin{array}{c}1,{\mathrm{rlq}}_{row}>0\\ 0,{\mathrm{rlq}}_{row}\&le;0\end{array},$ clq_colRepresent { d_r (x, y) } in col row in prospect alignment section quantity, $\mathrm{\&epsiv;}\left({\mathrm{clq}}_{col}\right)=\left\{\begin{array}{c}1,cl{q}_{col}>0\\ 0,cl{q}_{col}\&le;0\end{array}\right..$

Wherein, rlq_rowAnd clq_colAcquisition process be:

B1, to { d_r(x, y) } carry out binary conversion treatment, obtain { d_r(x, y) } binary image, be designated as { bi (x, y) }, The pixel value of the pixel that coordinate position in { bi (x, y) } is (x, y) is designated as bi (x, y), as { d_r(x, y) } in coordinate position When belonging to foreground target for the pixel of (x, y), make bi (x, y)=1, as { d_r(x, y) } in coordinate position be (x, y) pixel When point belongs to background region, make bi (x, y)=0.

B2,2 dilation operations that { bi (x, y) } is carried out successively with mathematical morphology, 4 erosion operations, 2 expansion fortune Calculate, obtain { bi'(x, y) }, wherein, bi'(x, y) represent that in { bi'(x, y) }, coordinate position is the pixel of the pixel of (x, y) Value.

The quantity of effective prospect line section in often going in b3, statistics { bi'(x, y) }, by the in { bi'(x, y) } In row row, the quantity of effective prospect line section is designated as rlq_row', rlq_row' acquisition process be: b3-1, make rlq_row' just Initial value is 0；B3-2, the 1st pixel of row row from { bi'(x, y) } start to scan to the right, are scanning the 1st picture During the pixel for 1 for the element value, using this pixel as the original position of the 1st section of prospect line section, and the row of this pixel are sat It is labeled as x₁', continue scanning to the right and obtain the 1st section of prospect line section till scanning the pixel that pixel value is 0, should Pixel value is that the row coordinate of 0 pixel is designated as x₂', if length x of the 1st section of prospect line section₂'-x₁' it is more than 0.005w, then really Fixed 1st section of prospect line section is effective prospect line section, and makes rlq_row'=rlq_row'+1, if the 1st section of prospect line section Length x₂'-x₁' it is less than or equal to 0.005w it is determined that the 1st section of prospect line section is invalid prospect line section；Continue to sweep to the right Retouch, with obtain the 1st section of prospect line section determine whether the 1st section of prospect line section is effective prospect line section identical side Formula, obtains all effective prospect line section in row row, and counts and obtain effective prospect line section in row row Quantity rlq_row'；Wherein, the initial value of row is 1,1≤row≤h, 1≤x₁'<x₂'≤w, rlq_row'=rlq_rowIn '+1 "=" is assignment.

Equally, in often going in statistics { bi'(x, y) } effective prospect alignment section quantity, by { bi'(x, y) } In col row, the quantity of effective prospect alignment section is designated as clq_col', clq_col' acquisition process be: b3-1), make clq_col' Initial value is 0；B3-2), the 1st pixel of the col row from { bi'(x, y) } starts to scan downwards, is scanning the 1st When individual pixel value is 1 pixel, using this pixel as the 1st section of prospect alignment section original position, and by this pixel Row coordinate is designated as y₁', continue scanning downwards and obtain the 1st section of prospect alignment section till scanning the pixel that pixel value is 0, The row coordinate of the pixel that this pixel value is 0 is designated as y₂', if length y of the 1st section of prospect alignment section₂'-y₁' it is more than 0.005h, Then determine that the 1st section of prospect alignment section is effective prospect alignment section, and make clq_col'=clq_col'+1, if the 1st section of prostatitis alignment Length y of section₂'-y₁' it is less than or equal to 0.005h it is determined that the 1st section of prospect alignment section is invalid prospect alignment section；Continue to Lower scanning, with obtain the 1st section of prospect alignment section determine whether the 1st section of prospect alignment section is that effective prospect alignment section is identical Mode, obtain all effective prospect alignment section in col row, and count and obtain in col row effectively prospect alignment Quantity clq of section_col'；Wherein, the initial value of col is 1,1≤col≤w, 1≤y₁'<y₂'≤h, clq_col'=clq_colIn '+1 "=" it is assignment.

B4, make rlq_row=rlq_row', make clq_col=clq_col'.

In above-mentioned, step is 2. in the right viewpoint parallax gray level image of every width stereo-picture in neutral body image data base Foreground target and background region acquisition modes and step 3. in stereo-picture to be evaluated right viewpoint parallax gray level image In foreground target identical with the acquisition modes of background region, by the every width stereo-picture in stereoscopic image data storehouse and to be evaluated Stereo-picture all as pending stereo-picture, then the prospect mesh in the right viewpoint parallax gray level image of pending stereo-picture The acquisition process of mark and background region is: obtains the grey level histogram of the right viewpoint parallax gray level image of pending stereo-picture； Then existing maximum variance between clusters (otsu) are adopted, and according to the right viewpoint parallax gray level image of pending stereo-picture Grey level histogram, obtains the intensity slicing threshold value of the right viewpoint parallax gray level image of pending stereo-picture, is designated as p_seg；Again will In the right viewpoint parallax gray level image of pending stereo-picture, pixel value is more than or equal to p_segPixel be defined as foreground pixel Point, and pixel value in the right viewpoint parallax gray level image of pending stereo-picture is less than p_segPixel be defined as background picture Vegetarian refreshments；Finally it is made up of the foreground target in the right viewpoint parallax gray level image of pending stereo-picture all foreground pixel points, It is made up of the background region in the right viewpoint parallax gray level image of pending stereo-picture all background pixels.

Here, intensity slicing threshold value p of the right viewpoint parallax gray level image of pending stereo-picture_segAcquisition process be:

X1, set up intensity slicing threshold value find object function, be designated as t, t=w_f×(μ-μ_f)²+w_b×(μ-μ_b)², wherein, w_f Represent that the total number of the foreground pixel point in the right viewpoint parallax gray level image of pending stereo-picture accounts for pending stereo-picture The ratio of the total number of pixel in right viewpoint parallax gray level image,μ_fRepresent pending stereogram The average of the pixel value of all foreground pixel points in the right viewpoint parallax gray level image of picture,w_b Represent that the total number of the background pixel in the right viewpoint parallax gray level image of pending stereo-picture accounts for pending stereo-picture The ratio of the total number of pixel in right viewpoint parallax gray level image,μ_bRepresent pending stereogram The average of the pixel value of all background pixels in the right viewpoint parallax gray level image of picture,μ Represent the average of the pixel value of all pixels point in the right viewpoint parallax gray level image of pending stereo-picture, μ=w_f×μ_f+ w_b×μ_b, hist_dG () represents the grey level histogram { hist of the right viewpoint parallax gray level image of pending stereo-picture_d(g) } in Pixel value is the number of the pixel of g, 0≤g_min≤g≤g_max≤ 255, g_minRepresent the right viewpoint parallax of pending stereo-picture Grey level histogram { the hist of gray level image_d(g) } in pixel number be more than 0 minimum pixel value, g_maxRepresent pending vertical Grey level histogram { the hist of the right viewpoint parallax gray level image of body image_d(g) } in pixel number be more than 0 maximum pixel Value；Grey level histogram { the hist of the right viewpoint parallax gray level image of pending stereo-picture_d(g) } in abscissa represent 0～ 255 gray level, ordinate represent that the number of pixel, each pillar represent the number of the pixel of different grey-scale.

X2, in interval [g_min,g_max] interior traversal t, the value of t when making t maximum is defined as pending stereo-picture Intensity slicing threshold value p of right viewpoint parallax gray level image_seg.

In above-mentioned, the step 2. middle side determining the scene mode belonging to every width stereo-picture in stereoscopic image data storehouse Formula is 3. middle with step to determine that the mode of the scene mode belonging to stereo-picture to be evaluated is identical, by stereoscopic image data storehouse Every width stereo-picture and stereo-picture to be evaluated are all as pending stereo-picture it is determined that belonging to pending stereo-picture The detailed process of scene mode is:

Z1, for the foreground target in the right viewpoint parallax gray level image of pending stereo-picture, if f_a ^*> 1 ° it is determined that Foreground target convex in screen and be in non-comfort zone；If 0 °≤f_a ^*< 1 ° it is determined that foreground target convex in screen and be in comfortable Area；If -1 ° of < f_a ^*< 0 ° it is determined that foreground target recessed in screen and be in comfort zone；If f_a ^*< -1 ° it is determined that foreground target is recessed In screen and be in non-comfort zone.

For the background region in the right viewpoint parallax gray level image of pending stereo-picture, if b_a ^*> 1 ° it is determined that background Region convex in screen and be in non-comfort zone；If 0 °≤b_a ^*< 1 ° it is determined that background region convex in screen and be in comfort zone；If- 1°<b_a ^*< 0 ° it is determined that background region recessed in screen and be in comfort zone；If b_a ^*< -1 ° it is determined that background region recessed in screen And it is in non-comfort zone.

Wherein, f_a ^*Represent the parallactic angle of the foreground target in the right viewpoint parallax gray level image of pending stereo-picture, f_a ^*= F-k, b_a ^*Represent the parallactic angle of the background region in the right viewpoint parallax gray level image of pending stereo-picture, b_a ^*=b-k, f table A binocular of leting others have a look at watches the convergent angle of foreground target,B represents human eye binocular viewing background The convergent angle in region,K represents the adjustment angle of human eye binocular, Arctan () is tan of negating, and p represents the interpupillary distance of human eye binocular, and l represents the width of display, and n represents the water of display Divide resolution equally, h represents human eye to the distance of display, f represents in the right viewpoint parallax gray level image of pending stereo-picture The mean parallax amplitude of foreground target, f in units of pixel, $f=\frac{\underset{x=1}{\overset{w}{\&sigma;}}\underset{y=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}\left({d_r}^{*}\right(x,y)-p_{seg})\&times;{d_r}^{*}(x,y)}{\underset{x=1}{\overset{w}{\&sigma;}}\underset{y=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}\left({d_r}^{*}\right(x,y)-p_{seg})},\mathrm{\&epsiv;}\left({d_r}^{*}\right(x,y)-p_{seg})=\left\{\begin{array}{c}1,{d_r}^{*}(x,y)-p_{seg}\&greaterequal;0\\ 0,{d_r}^{*}(x,y)-p_{seg}<0\end{array}\right.,$ B represents and treats Process stereo-picture right viewpoint parallax gray level image in background region mean parallax amplitude, b in units of pixel, $b=\frac{\underset{x=1}{\overset{w}{\&sigma;}}\underset{y=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}(p_{seg}-{d_r}^{*}(x,y\left)\right)\&times;{d_r}^{*}(x,y)}{\underset{x=1}{\overset{w}{\&sigma;}}\underset{y=1}{\overset{h}{\&sigma;}}\mathrm{\&epsiv;}(p_{seg}-{d_r}^{*}(x,y\left)\right)},\mathrm{\&epsiv;}(p_{seg}-{d_r}^{*}(x,y\left)\right)=\left\{\begin{array}{c}1,p_{seg}-{d_r}^{*}(x,y)\&greaterequal;0\\ 0,p_{seg}-{d_r}^{*}(x,y)<0\end{array}\right.,$ d_r ^*(x,y) Represent that in the right viewpoint parallax gray level image of pending stereo-picture, coordinate position is the pixel value of the pixel of (x, y), Fig. 2 gives Go out the geometric representation of f, b, k, p, h, f, b.

Z2, concavo-convex with respect to screen according to the foreground target in the right viewpoint parallax gray level image of pending stereo-picture Property and whether be in comfort zone, whether background region with respect to the concavity and convexity of screen and is in comfort zone, determines pending Scene mode belonging to stereo-picture, if particularly as follows: foreground target convex in screen and be in non-comfort zone, background region recessed in screen Curtain and be in non-comfort zone, i.e. f_a ^*> 1 ° and b_a ^*< -1 ° it is determined that pending stereo-picture belongs to the 1st kind of scene mode；If front Scape target convex in screen and be in non-comfort zone, background region convex in screen and be in non-comfort zone, i.e. f_a ^*> 1 ° and b_a ^*> 1 °, Then determine that pending stereo-picture belongs to the 2nd kind of scene mode；If foreground target recessed in screen and be in non-comfort zone, rear scenic spot Domain recessed in screen and be in non-comfort zone, i.e. f_a ^*< -1 ° and b_a ^*< -1 ° it is determined that pending stereo-picture belongs to the 3rd kind of scene Pattern；If foreground target convex in screen and be in non-comfort zone, background region recessed in screen and be in comfort zone, i.e. f_a ^*> 1 ° and- 1°≤b_a ^*< 0 ° it is determined that pending stereo-picture belongs to the 4th kind of scene mode；If foreground target convex in screen and be in non-relax Suitable area, background region convex in screen and be in comfort zone, i.e. f_a ^*> 1 ° and 0 °≤b_a ^*≤ 1 ° it is determined that pending stereo-picture belongs to In the 5th kind of scene mode；If foreground target convex in screen and be in comfort zone, background region recessed in screen and be in non-comfortable Area, i.e. 0 °≤f_a ^*≤ 1 ° and b_a ^*< -1 ° it is determined that pending stereo-picture belongs to the 6th kind of scene mode；If foreground target recessed in Screen and be in comfort zone, background region recessed in screen and be in non-comfort zone, that is, -1 °≤f_a ^*< 0 ° and b_a ^*< -1 ° it is determined that Pending stereo-picture belongs to the 7th kind of scene mode；If foreground target convex in screen and be in comfort zone, background region recessed in screen Curtain and be in comfort zone, i.e. 0 °≤f_a ^*≤ 1 ° and -1 °≤b_a ^*< 0 ° it is determined that pending stereo-picture belongs to the 8th kind of scene mould Formula；If foreground target convex in screen and be in comfort zone, background region convex in screen and be in comfort zone, i.e. 0 °≤f_a ^*≤ 1 ° and 0°≤b_a ^*≤ 1 ° it is determined that pending stereo-picture belongs to the 9th kind of scene mode；If foreground target recessed in screen and be in comfortable Area, background region recessed in screen and be in comfort zone, that is, -1 °≤f_a ^*< 0 ° and -1 °≤b_a ^*< 0 ° it is determined that pending stereogram As belonging to the 10th kind of scene mode.

In the present embodiment, objective parameters are commonly used as evaluation index by the use of assessment 5 of image quality evaluating method, that is, Pearson coefficient correlation under the conditions of nonlinear regression (pearson linear correlation coefficient, Plcc), spearman coefficient correlation (spearman rank order correlation coefficient, srocc), Kendall coefficient correlation (kendall rank-order correlation coefficient, krocc), average absolute value Error (mean absolute error, mae), root-mean-square error (root mean squared error, rmse).Plcc is anti- Reflect the correlation of objective evaluation predicted value and subjective assessment value；Srocc and krocc reflection objective evaluation predicted value and subjective assessment The monotonicity of value, uniformity；Mae and rmse reflects the accuracy of objective evaluation predicted value.Obtain 120 width using the inventive method The respective final visual comfort evaluation and foreca value of stereo-picture, the final visual comfort to this 120 width stereo-picture Evaluation and foreca value does five parameter logistic function nonlinear fittings, and plcc, srocc and krocc value is higher, and mae and rmse Correlation between the evaluation result of value less explanation the inventive method and mean subjective scoring average is better.Table 1 gives this The evaluation index value of inventive method, illustrates the result of the objective evaluation result and human eye subjective perception obtaining using the inventive method More consistent, illustrate the validity of the inventive method well.

Table 1 evaluation index value

Evaluation index	plcc	srocc	krocc	mae	rmse
							0.919	0.909	0.748	0.237	0.318

Claims (8)

1. a kind of stereo image vision comfort level evaluation method based on scene mode classification is it is characterised in that include following walking Rapid:

1. 10 kinds of scene modes of the natural scene shown by three-dimensional display are determined, the 1st kind of scene mode is stereo-picture Foreground target in right viewpoint parallax gray level image convex in screen and be in non-comfort zone, background region recessed in screen and be in non- Comfort zone；2nd kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and be in Non- comfort zone, background region convex in screen and be in non-comfort zone；3rd kind of scene mode is the right viewpoint parallax ash of stereo-picture Foreground target in degree image recessed in screen and be in non-comfort zone, background region recessed in screen and be in non-comfort zone；4th kind Scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and be in non-comfort zone, background Region recessed in screen and be in comfort zone；5th kind of scene mode is the prospect in the right viewpoint parallax gray level image of stereo-picture Target convex in screen and be in non-comfort zone, background region convex in screen and be in comfort zone；6th kind of scene mode is stereogram Foreground target in the right viewpoint parallax gray level image of picture convex in screen and be in comfort zone, background region recessed in screen and be in Non- comfort zone；7th kind of scene mode be foreground target in the right viewpoint parallax gray level image of stereo-picture recessed in screen and place In comfort zone, background region recessed in screen and be in non-comfort zone；8th kind of scene mode is the right viewpoint parallax ash of stereo-picture Foreground target in degree image convex in screen and be in comfort zone, background region recessed in screen and be in comfort zone；9th kind of scene Pattern be foreground target in the right viewpoint parallax gray level image of stereo-picture convex in screen and to be in comfort zone, background region convex In screen and be in comfort zone；10th kind of scene mode is that the foreground target in the right viewpoint parallax gray level image of stereo-picture is recessed In screen and be in comfort zone, background region recessed in screen and be in comfort zone；

Here, for the foreground target in the right viewpoint parallax gray level image of stereo-picture, if f_a ^*> 1 ° it is determined that foreground target Convex in screen and be in non-comfort zone；If 0 °≤f_a ^*< 1 ° it is determined that foreground target convex in screen and be in comfort zone；If -1 ° < f_a ^*< 0 ° it is determined that foreground target recessed in screen and be in comfort zone；If f_a ^*< -1 ° it is determined that foreground target recessed in screen and place In non-comfort zone；

For the background region in the right viewpoint parallax gray level image of stereo-picture, if b_a ^*> 1 ° it is determined that background region convex in screen Curtain and be in non-comfort zone；If 0 °≤b_a ^*< 1 ° it is determined that background region convex in screen and be in comfort zone；If -1 ° of <b_a ^*< 0 °, Then determine background region recessed in screen and be in comfort zone；If b_a ^*< -1 ° it is determined that background region recessed in screen and be in non-relax Suitable area；

Wherein, f_a ^*Represent the parallactic angle of the foreground target in the right viewpoint parallax gray level image of stereo-picture, f_a ^*=f-k, b_a ^*Table Show the parallactic angle of the background region in the right viewpoint parallax gray level image of stereo-picture, b_a ^*=b-k, f represent that human eye binocular is watched The convergent angle of foreground target,B represents that human eye binocular watches the convergent angle of background region,K represents the adjustment angle of human eye binocular,Arctan () is just to negate Cut function, p represents the interpupillary distance of human eye binocular, l represents the width of display, n represents the horizontal resolution of display, h represents that human eye arrives The distance of display, f represents the mean parallax amplitude of the foreground target in the right viewpoint parallax gray level image of stereo-picture, and f is with picture Vegetarian refreshments is unit, B represents the mean parallax amplitude of the background region in the right viewpoint parallax gray level image of stereo-picture, b in units of pixel,d_r ^*(x, y) table Show that in the right viewpoint parallax gray level image of stereo-picture, coordinate position is the pixel value of the pixel of (x, y)；

2. every width stereo-picture is selected to have a stereoscopic image data storehouse of the mean subjective scoring average of visual comfort；Then Parallactic angle according to the foreground target in the right viewpoint parallax gray level image of the every width stereo-picture in stereoscopic image data storehouse and The parallactic angle of background region, determines the scene mode belonging to every width stereo-picture in stereoscopic image data storehouse；Further according to solid The prospect mesh in the respective right viewpoint parallax gray level image of all stereo-pictures of every kind of scene mode is belonged in image data base Target parallactic angle and the parallactic angle of background region, set up the visual comfort evaluation model under every kind of scene mode, by n field Visual comfort evaluation model under scape pattern is described as follows: smmo_n=(4.2028-u (q_n))-v(q_n)×d_a(q_n)+0.1912 ×ln(w_a)-0.0208×d_a(q_n)×ln(w_a), wherein, 1≤n≤10, smmo_nRepresent that the vision under n scene mode is relaxed The output of appropriate evaluation model, u (q_n) and v (q_n) it is constant, d_a(q_n) represent that a width to be entered belongs to n scene mould The global disparity angle of the right viewpoint parallax gray level image of the stereo-picture to be evaluated of formula, d_a(q_n)=q_n×|f_a|+(1-q_n)×| b_a|, q_nRepresent the weight under n scene mode, f_aThe width representing to be entered belongs to the to be evaluated of n scene mode The parallactic angle of the foreground target in the right viewpoint parallax gray level image of stereo-picture, b_aThe width representing to be entered belongs to n The parallactic angle of the background region in the right viewpoint parallax gray level image of the stereo-picture to be evaluated of scene mode, w_aExpression is treated defeated The width entering belongs to the foreground target in the right viewpoint parallax gray level image of the stereo-picture to be evaluated of n scene mode Width angle, symbol " | | " it is the symbol that takes absolute value；

3. the right viewpoint parallax gray level image of stereo-picture to be evaluated is designated as { d_r(x, y) }, wherein, 1≤x≤w, 1≤y≤ H, w represent the width of stereo-picture to be evaluated, and it is consistent with the width of the every width stereo-picture in stereoscopic image data storehouse, h Represent the height of stereo-picture to be evaluated, its with stereoscopic image data storehouse in every width stereo-picture highly consistent, d_r(x, Y) represent { d_r(x, y) } in coordinate position be (x, y) pixel pixel value；Then adopt and step 2. middle identical side Formula, determines the scene mode belonging to stereo-picture to be evaluated；Further according to the scene mode belonging to stereo-picture to be evaluated, select Take the visual comfort evaluation model under this scene mode；Then according to the visual comfort evaluation model under this scene mode and {d_r(x, y) } in the parallactic angle of foreground target, { d_r(x, y) } in the parallactic angle of background region and { d_r(x, y) } in prospect The width angle of target, calculates the visual comfort evaluation and foreca value of stereo-picture to be evaluated, is designated as smmo it is assumed that to be evaluated Stereo-picture belongs to n scene mode, then smmo=(4.2028-u (q_n))-v(q_n)×d_a'(q_n)+0.1912×ln (w_a')-0.0208×d_a'(q_n)×ln(w_a'), wherein, d_a'(q_n) represent { d_r(x, y) } global disparity angle, d_a'(q_n)=q_n ×|f_a'|+(1-q_n)×|b_a' |, f_a' represent { d_r(x, y) } in foreground target parallactic angle, b_a' represent { d_r(x, y) } in The parallactic angle of background region, w_a' represent { d_r(x, y) } in foreground target width angle；

4. visual comfort evaluation and foreca value smmo of stereo-picture to be evaluated is modified, by repairing of stereo-picture to be evaluated Visual comfort evaluation and foreca value after just is designated as smm, Wherein, max () is to take max function, and p represents that the visual comfort under the scene mode belonging to stereo-picture to be evaluated is commented The tortuosity attenuation coefficient of valency model, s_r' represent { d_r(x, y) } in foreground target average prospect line hop count, s_c' represent {d_r(x, y) } in foreground target average prospect alignment hop count, t_fRepresent the parallactic angle threshold value setting, t_rRepresent before setting Scape line hop count threshold value, t_cRepresent the prospect alignment hop count threshold value setting.

2. a kind of stereo image vision comfort level evaluation method based on scene mode classification according to claim 1, its It is characterised by described step 2. middle q_nAcquisition process be:

2. -1, assume that the total width number belonging to the stereo-picture of n scene mode in stereoscopic image data storehouse is m', wherein, m' ≥1；

2. -2, make q_nInitial value be 0.1, make q_nInitial value be 0；

2. -3, calculating the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene mode The right viewpoint parallax gray level image of every width stereo-picture global disparity angle, the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene mode the right viewpoint parallax gray level image of m' width stereo-picture complete Office's parallactic angle is designated as d_m',a(q_n), d_m',a(q_n)=q_n×|f_m',a|+(1-q_n)×|b_m',a|, wherein, 1≤m'≤m', f_m',aWith b_m',aThe corresponding right viewpoint parallax gray scale representing the m' width stereo-picture belonging to n scene mode in stereoscopic image data storehouse The parallactic angle of the foreground target in image and the parallactic angle of background region, symbol " | | " it is the symbol that takes absolute value；

2. -4, calculating the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene mode Every width stereo-picture eliminate the comfort level objective evaluation value of parallax linear effect, the weight under n scene mode is taken It is worth for q_nWhen stereoscopic image data storehouse in belong to the m' width stereo-picture of n scene mode and eliminate parallax linear effect Comfort level objective evaluation value is designated as vc_h(d_m',_a(q_n),w_a), vc_h(d_m',a(q_n),w_a)=4.2028+0.1912 × ln (w_a)- 0.0208×d_m',a(q_n)×ln(w_a)；Then calculating the weight value under n scene mode is q_nWhen stereoscopic image data storehouse In belong to n scene mode the parallax linear effect of every width stereo-picture comfort level objective evaluation value, by n scene Weight value under pattern is q_nWhen stereoscopic image data storehouse in belong to n scene mode m' width stereo-picture parallax The comfort level objective evaluation value of linear effect is designated as err_m', err_m'=vc_h(d_m'<a(q_n),w_a)-mos_m', wherein, mos_m'Represent Weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene mode m' width three-dimensional The comfort level subjective assessment value of image；Adopt least square method again, be q to the weight value under n scene mode_nShi Liti The global disparity of the respective right viewpoint parallax gray level image of all stereo-pictures of n scene mode is belonged in image data base The comfort level objective evaluation value of angle and respective parallax linear effect carries out linear fit, obtains the power under n scene mode Refetching value is q_nWhen stereoscopic image data storehouse in belong to a fitting a straight line corresponding to all stereo-pictures of n scene mode Equation: err=u (q_n)+v(q_n)×d_a(q_n), wherein, err be for represent parallax to the linear effect of objective comfort level because Variable, u (q_n) and v (q_n) it is err=u (q_n)+v(q_n)×d_a(q_n) in constant, d_a(q_n) it is for representing n scene Weight value under pattern is q_nWhen stereoscopic image data storehouse in belong to n scene mode the right side of arbitrary width stereo-picture regard The independent variable at the global disparity angle of point parallax gray level image；

2. it is -5, q according to the weight value under n scene mode_nWhen stereoscopic image data storehouse in belong to n scene mode A fitting a straight line equation corresponding to all stereo-pictures, obtaining the weight value under n scene mode is q_nShi Liti The comfort level objective evaluation equation of all stereo-pictures of n scene mode: smmo is belonged in image data base_n(q_n)=vc_h (d_a(q_n),w_a)-err=(4.2028-u (q_n))-v(q_n)×d_a(q_n)+0.1912×ln(w_a)-0.0208×d_a(q_n)×ln (w_a), wherein, smmo_n(q_n) it is the comfort level objective evaluation value being superimposed parallax linear effect and parallax non-linear effects, vc_h (d_a(q_n),w_a) represent that the weight value under n scene mode is q_nWhen stereoscopic image data storehouse in belong to n scene mould Arbitrary width stereo-picture of formula eliminates the comfort level objective evaluation value of parallax linear effect；Then measure n scene mode Under weight value be q_nWhen stereoscopic image data storehouse in belong to n scene mode all stereo-pictures comfort level objective The fitting degree of evaluation of estimate and corresponding comfort level subjective assessment value；

2. -6, make q_n=q_n+ 0.1, it is then back to step and 2. -3 continue executing with, until q_nTerminate during equal to 1.1, obtain n field Corresponding matching journey when weight value under scape pattern is respectively 0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0 Then corresponding for best fit degree weight value is assigned to q by degree_n, wherein, q_n=q_nIn+0.1 "=" it is assignment；

Described step 2. middle u (q_n) value be best fit degree corresponding fitting a straight line equation in Monomial coefficient；Institute The step stated 2. middle v (q_n) value be best fit degree corresponding fitting a straight line equation in constant.

3. a kind of stereo image vision comfort level evaluation method based on scene mode classification according to claim 1 and 2, It is characterized in that described step is 2. in the right viewpoint parallax gray level image of every width stereo-picture in neutral body image data base Foreground target and background region acquisition modes and described step 3. in stereo-picture to be evaluated right viewpoint parallax ash Foreground target in degree image is identical with the acquisition modes of background region, by the every width stereo-picture in stereoscopic image data storehouse and Stereo-picture to be evaluated all as pending stereo-picture, then in the right viewpoint parallax gray level image of pending stereo-picture The acquisition process of foreground target and background region is: the gray scale obtaining the right viewpoint parallax gray level image of pending stereo-picture is straight Fang Tu；Then maximum variance between clusters are adopted, and straight according to the gray scale of the right viewpoint parallax gray level image of pending stereo-picture Fang Tu, obtains the intensity slicing threshold value of the right viewpoint parallax gray level image of pending stereo-picture, is designated as p_seg；Again will be pending In the right viewpoint parallax gray level image of stereo-picture, pixel value is more than or equal to p_segPixel be defined as foreground pixel point, and Pixel value in the right viewpoint parallax gray level image of pending stereo-picture is less than p_segPixel be defined as background pixel； Foreground target in the last right viewpoint parallax gray level image being made up of pending stereo-picture all foreground pixel points, by owning Background pixel constitutes the background region in the right viewpoint parallax gray level image of pending stereo-picture.

4. a kind of stereo image vision comfort level evaluation method based on scene mode classification according to claim 3, its It is characterised by intensity slicing threshold value p of the described right viewpoint parallax gray level image of pending stereo-picture_segAcquisition process For:

X1, set up intensity slicing threshold value find object function, be designated as t, t=w_f×(μ-μ_f)²+w_b×(μ-μ_b)², wherein, w_fRepresent The total number of the foreground pixel point in the right viewpoint parallax gray level image of pending stereo-picture accounts for the right side of pending stereo-picture The ratio of the total number of pixel in viewpoint parallax gray level image,μ_fRepresent pending stereo-picture Right viewpoint parallax gray level image in the pixel value of all foreground pixel points average,w_bTable Show that the total number of the background pixel in the right viewpoint parallax gray level image of pending stereo-picture accounts for pending stereo-picture The ratio of the total number of pixel in right viewpoint parallax gray level image,μ_bRepresent pending stereogram The average of the pixel value of all background pixels in the right viewpoint parallax gray level image of picture,μ Represent the average of the pixel value of all pixels point in the right viewpoint parallax gray level image of pending stereo-picture, μ=w_f×μ_f+ w_b×μ_b, hist_dG () represents the grey level histogram { hist of the right viewpoint parallax gray level image of pending stereo-picture_d(g) } in Pixel value is the number of the pixel of g, 0≤g_min≤g≤g_max≤ 255, g_minRepresent the right viewpoint parallax of pending stereo-picture Grey level histogram { the hist of gray level image_d(g) } in pixel number be more than 0 minimum pixel value, g_maxRepresent pending vertical Grey level histogram { the hist of the right viewpoint parallax gray level image of body image_d(g) } in pixel number be more than 0 maximum pixel Value；

X2, in interval [g_min,g_max] interior traversal t, the right side that the value of t when making t maximum is defined as pending stereo-picture regards Intensity slicing threshold value p of point parallax gray level image_seg.

5. a kind of stereo image vision comfort level evaluation method based on scene mode classification according to claim 3, its It is characterised by the described step 2. middle mode determining the scene mode belonging to every width stereo-picture in stereoscopic image data storehouse Identical with the mode of the scene mode belonging to described step 3. middle determination stereo-picture to be evaluated, by stereoscopic image data storehouse In every width stereo-picture and stereo-picture to be evaluated all as pending stereo-picture it is determined that pending stereo-picture institute The detailed process of the scene mode belonging to is:

Z1, for the foreground target in the right viewpoint parallax gray level image of pending stereo-picture, if f_a ^*> 1 ° it is determined that prospect Target convex in screen and be in non-comfort zone；If 0 °≤f_a ^*< 1 ° it is determined that foreground target convex in screen and be in comfort zone；If- 1°<f_a ^*< 0 ° it is determined that foreground target recessed in screen and be in comfort zone；If f_a ^*< -1 ° it is determined that foreground target recessed in screen And it is in non-comfort zone；

For the background region in the right viewpoint parallax gray level image of pending stereo-picture, if b_a ^*> 1 ° it is determined that background region Convex in screen and be in non-comfort zone；If 0 °≤b_a ^*< 1 ° it is determined that background region convex in screen and be in comfort zone；If -1 ° < b_a ^*< 0 ° it is determined that background region recessed in screen and be in comfort zone；If b_a ^*< -1 ° it is determined that background region recessed in screen and place In non-comfort zone；

Wherein, f_a ^*Represent the parallactic angle of the foreground target in the right viewpoint parallax gray level image of pending stereo-picture, f_a ^*=f-k, b_a ^* Represent the parallactic angle of the background region in the right viewpoint parallax gray level image of pending stereo-picture, b_a ^*=b-k, f represent human eye binocular The convergent angle of viewing foreground target,B represents that human eye binocular watches the convergent angle of background region,K represents the adjustment angle of human eye binocular,Arctan () is just to negate Cut function, p represents the interpupillary distance of human eye binocular, l represents the width of display, n represents the horizontal resolution of display, h represents that human eye arrives The distance of display, f represents the mean parallax amplitude of the foreground target in the right viewpoint parallax gray level image of pending stereo-picture, f In units of pixel,b Represent the mean parallax amplitude of the background region in the right viewpoint parallax gray level image of pending stereo-picture, b with pixel is Unit,d_r ^* (x, y) represents the pixel value of the pixel that coordinate position in the right viewpoint parallax gray level image of pending stereo-picture is (x, y)；

Z2, according to the foreground target in the right viewpoint parallax gray level image of pending stereo-picture with respect to screen concavity and convexity with And whether be in comfort zone, whether background region with respect to the concavity and convexity of screen and is in comfort zone, determines pending solid Scene mode belonging to image, if particularly as follows: foreground target convex in screen and be in non-comfort zone, background region recessed in screen and It is in non-comfort zone it is determined that pending stereo-picture belongs to the 1st kind of scene mode；If foreground target convex in screen and be in non- Comfort zone, background region convex in screen and be in non-comfort zone it is determined that pending stereo-picture belongs to the 2nd kind of scene mode； If foreground target recessed in screen and be in non-comfort zone, background region recessed in screen and be in non-comfort zone it is determined that pending Stereo-picture belongs to the 3rd kind of scene mode；If foreground target convex in screen and be in non-comfort zone, background region recessed in screen and It is in comfort zone it is determined that pending stereo-picture belongs to the 4th kind of scene mode；If foreground target convex in screen and be in non-relax Suitable area, background region convex in screen and be in comfort zone it is determined that pending stereo-picture belongs to the 5th kind of scene mode；If front Scape target convex in screen and be in comfort zone, background region recessed in screen and be in non-comfort zone it is determined that pending stereogram As belonging to the 6th kind of scene mode；If foreground target recessed in screen and be in comfort zone, background region recessed in screen and be in non-relaxing Suitable area is it is determined that pending stereo-picture belongs to the 7th kind of scene mode；If foreground target convex in screen and be in comfort zone, after Scene area recessed in screen and be in comfort zone it is determined that pending stereo-picture belongs to the 8th kind of scene mode；If foreground target is convex In screen and be in comfort zone, background region convex in screen and be in comfort zone it is determined that pending stereo-picture belongs to the 9th kind Scene mode；If foreground target recessed in screen and be in comfort zone, background region recessed in screen and be in comfort zone it is determined that treating Process stereo-picture and belong to the 10th kind of scene mode.

6. a kind of stereo image vision comfort level evaluation method based on scene mode classification according to claim 5, its { d in being characterised by described step 3._r(x, y) } in foreground target width angleWherein, Arctan () is to negate tan, and h represents human eye to the distance of display, w_frontRepresent { d_r(x, y) } in foreground target Mean breadth, w_frontAcquisition process be:

A1, to { d_r(x, y) } carry out binary conversion treatment, obtain { d_r(x, y) } binary image, be designated as { bi (x, y) }, by { bi (x, y) } in coordinate position be that the pixel value of pixel of (x, y) is designated as bi (x, y), as { d_r(x, y) } in coordinate position be (x, Y), when pixel belongs to foreground target, make bi (x, y)=1, as { d_r(x, y) } in coordinate position be (x, y) pixel belong to When background region, make bi (x, y)=0；

A2, line scans are entered to { bi (x, y) }, for the row row in { bi (x, y) }, from the 1st pixel of row row Start to scan to the right, when scanning the pixel that the 1st pixel value is 1, using this pixel as the 1st section of prospect width line segment Original position, and the row coordinate of this pixel is designated as x₁, continue scanning to the right until scanning the pixel that pixel value is 0 Till obtain the 1st section of prospect width line segment, the row coordinate of the pixel that this pixel value is 0 is designated as x₂, by the 1st section of prospect width The length of line segment is designated as wl₁, wl₁=x₂-x₁；Continue scan to the right, with obtain the 1st section of prospect width line segment identical mode, Obtain all prospect width line segments in row row；Wherein, the initial value of row is 1,1≤row≤h, 1≤x₁<x₂≤w；

A3, length in { bi (x, y) } is less than all prospect width line segments of 0.002w and length be more than 0.995w all before Scape width line segment removes；Then by length, order from small to large is ranked up to remaining all prospect width line segments, in taking Between 80% prospect width line segment constitute prospect width line segment aggregate, be designated as { wl'_n, wherein, wl'_nRepresent { wl'_nIn n-th The length of Duan Qianjing width line segment, 1≤n≤n', n' represent { wl'_nIn total hop count of prospect width line segment of comprising；

A4, calculating { wl'_nIn the length of all prospect width line segments mean value, be designated as w_front', Then by w_front' as { d_r(x, y) } in foreground target mean breadth w_frontEven, w_front=w_front'.

7. a kind of stereo image vision comfort level evaluation method based on scene mode classification according to claim 6, its { d in being characterised by described step 4._r(x, y) } in foreground target average prospect line hop count{d_r(x, y) } in foreground target average prospect alignment hop countIts In, 1≤row≤h, 1≤col≤w, rlq_rowRepresent { d_r(x, y) } in row row in prospect line section quantity,clq_colRepresent { d_r(x, y) } in col row in prospect alignment section quantity,

Wherein, rlq_rowAnd clq_colAcquisition process be:

B1, to { d_r(x, y) } carry out binary conversion treatment, obtain { d_r(x, y) } binary image, be designated as { bi (x, y) }, by { bi (x, y) } in coordinate position be that the pixel value of pixel of (x, y) is designated as bi (x, y), as { d_r(x, y) } in coordinate position be (x, Y), when pixel belongs to foreground target, make bi (x, y)=1, as { d_r(x, y) } in coordinate position be (x, y) pixel belong to When background region, make bi (x, y)=0；

B2,2 dilation operations that { bi (x, y) } is carried out successively with mathematical morphology, 4 erosion operations, 2 dilation operations, obtain Arrive { bi'(x, y) }, wherein, bi'(x, y) represent that in { bi'(x, y) }, coordinate position is the pixel value of the pixel of (x, y)；

The quantity of effective prospect line section in often going in b3, statistics { bi'(x, y) }, by the row row in { bi'(x, y) } In the quantity of effective prospect line section be designated as rlq_row', rlq_row' acquisition process be: b3-1, make rlq_row' initial value be 0；B3-2, the 1st pixel of row row from { bi'(x, y) } start to scan to the right, scanning the 1st pixel value are During 1 pixel, using this pixel as the original position of the 1st section of prospect line section, and the row coordinate of this pixel is designated as x₁', continue scanning to the right and obtain the 1st section of prospect line section till scanning the pixel that pixel value is 0, by this pixel value The row coordinate of the pixel for 0 is designated as x₂', if length x of the 1st section of prospect line section₂'-x₁' more than 0.005w it is determined that the 1st Duan Qianjing line section is effective prospect line section, and makes rlq_row'=rlq_row'+1, if the length of the 1st section of prospect line section x₂'-x₁' it is less than or equal to 0.005w it is determined that the 1st section of prospect line section is invalid prospect line section；Continue to scan to the right, With with obtain the 1st section of prospect line section determine whether the 1st section of prospect line section is effective prospect line section identical mode, Obtain all effective prospect line section in row row, and count the quantity obtaining effective prospect line section in row row rlq_row'；Wherein, the initial value of row is 1,1≤row≤h, 1≤x₁'<x₂'≤w, rlq_row'=rlq_rowIn '+1 "=" be Assignment；

Equally, in often going in statistics { bi'(x, y) } effective prospect alignment section quantity, by the col in { bi'(x, y) } In row, the quantity of effective prospect alignment section is designated as clq_col', clq_col' acquisition process be: b3-1), make clq_col' initial It is worth for 0；B3-2), the 1st pixel of the col row from { bi'(x, y) } starts to scan downwards, is scanning the 1st picture During the pixel for 1 for the element value, using this pixel as the 1st section of prospect alignment section original position, and the row seat by this pixel It is labeled as y₁', continue scanning downwards and obtain the 1st section of prospect alignment section till scanning the pixel that pixel value is 0, should Pixel value is that the row coordinate of 0 pixel is designated as y₂', if length y of the 1st section of prospect alignment section₂'-y₁' it is more than 0.005h, then really Fixed 1st section of prospect alignment section is effective prospect alignment section, and makes clq_col'=clq_col'+1, if the 1st section of prostatitis alignment section Length y₂'-y₁' it is less than or equal to 0.005h it is determined that the 1st section of prospect alignment section is invalid prospect alignment section；Continue to sweep downwards Retouch, with obtain the 1st section of prospect alignment section determine whether the 1st section of prospect alignment section is effective prospect alignment section identical side Formula, obtains all effective prospect alignment section in col row, and counts and obtain in col row effective prospect alignment section Quantity clq_col'；Wherein, the initial value of col is 1,1≤col≤w, 1≤y₁'<y₂'≤h, clq_col'=clq_colIn '+1 "=" is assignment；

B4, make rlq_row=rlq_row', make clq_col=clq_col'.

8. a kind of stereo image vision comfort level evaluation method based on scene mode classification according to claim 7, its P=1.6, t is taken in being characterised by described step 4._f=2.0, t_r=2, t_c=1.5.