Patent Publication Number: US-6657677-B1

Title: Method and apparatus for improving conversion from SD to HDTV

Description:
RELATED APPLICATIONS 
     A related application, entitled METHOD AND APPARATUS FOR EDGE DETECTION, and having the same drawings and detailed description as the present application, is being filed concurrently herewith by the same inventors. 
     A second patent application by the same inventors as the present application and entitled “SYSTEM AND METHOD FOR IMPROVING THE SHARPNESS OF A VIDEO IMAGE” (ID 701025) is being filed concurrently herewith, and is incorporated herein by reference. 
     The present application claims benefit of provisional patent applications serial No. 60/175,777, filed Jan. 12, 2000, and No. 60/187,422, filed Mar. 7, 2000, which are both also incorporated herein by reference. 
    
    
     I. BACKGROUND OF THE INVENTION 
     A. Field of the Invention 
     The invention relates to the field of image enhancement. 
     B. Related Art 
     U.S. Pat. No. 5,666,164 shows an image enhancement technique for conversion from standard definition (“SD”) to high definition television (“HDTV”) signals. 
     II. SUMMARY OF THE INVENTION 
     It is an object of the invention to achieve further improvement with respect to image enhancement. 
     This object is achieved through a device that includes an edge detection unit, a non-linear enhancement unit for enhancing edges detected by the edge detection unit, and a linear enhancement device for enhancing non-edge areas identified by the edge detection unit. 
     Advantageously the linear device is a peaking unit. Also advantageously the non-linear device is an LTI unit. 
     Preferably the LTI unit is a sub-pixel level unit. 
     The device according to the invention can be used to improve SD to HDTV conversion. 
     Further objects and advantages will be apparent from the following. 
    
    
     III. BRIEF DESCRIPTION OF THE DRAWING 
     The invention will now be described by way of non-limiting example with reference to the following drawings. 
     FIG. 1 shows an overview of a conversion unit for converting an SD signal to an HDTV signal. 
     FIG. 2 shows more detail of the up-sampling unit. 
     FIGS. 3 through 8 show operation of the linear interpolator. 
     FIG. 9 shows a device for achieving sharpness enhancement. 
     FIG. 10 is a flowchart relating to edge detection. 
     FIG. 11 shows the difference between the effects of standard and low pass Laplace operators. 
     FIG. 12 shows the effect of different values of THRESHOLD 2 . 
     FIGS. 13A-F and FIGS. 14A-F show rules for edge linking. 
    
    
     IV. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     FIG. 1 shows an overview of a device in accordance with the invention. An SD signal is fed to an interlaced to progressive converter  110 . More information about interlaced to progressive converters can be found in G. De Haan and E. B. Bellers, “De-Interlacing—an overview”, Proc. IEEE, vol. 86, pp. 1839-1857 (September 1998). At box  111 , up-sampling is achieved by linear interpolation. Up-sampling can be performed separately for the horizontal and vertical directions prior to sharpness enhancement. The signal is then subjected to sharpness enhancement at box  112 . Then a progressive to interlaced converter, which just takes every other line for each frame, turns the signal into an HDTV signal at  113 . The boxes of FIG. 1 can be realized with hardwired circuits, or as code within a general purpose processor. 
     FIG. 2 shows more detail of the structure of box  111 . Box  111  includes a conventional zero insertion unit  201  and a conventional anti-aliasing filter  102 . The anti-aliasing filter preferably has the following coefficients: 
     
       
         
           
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     FIGS. 3 through 8 show the operation of the linear interpolator. FIG. 3 a  shows a time domain version of the input signal to the linear interpolator with samples 0 through n of different amplitudes. FIG. 3 b  shows a frequency domain version of the same input signal. The frequency domain version falls to zero at the value f s0 /2 where f s0  is the original sampling frequency. 
     FIG. 4 a  shows the action of the zero inserter  201  in the time domain. FIG. 4 b  shows the result of the zero inserter in the frequency domain. The zero inserter causes the appearance of a second peak in the frequency domain, having a cutoff at f sn /2, where f sn  is the new sample frequency due to interpolation, in other words, the factor of upscaling in this example is two. 
     FIG. 5 shows the behavior of the ideal anti-aliasing filter in the frequency domain. 
     FIG. 6 a  shows, in the time domain, the result of the ideal anti-aliasing filter on the output of the zero inserter. FIG. 6 b  shows the same signal as FIG. 6 a , except in the frequency domain. 
     FIG. 7 shows the profile of a real anti-aliasing filter in the frequency domain. 
     FIG. 8 a  shows, in the time domain, the result of a real anti-aliasing filter on the output of the zero inserter. FIG. 8 b  shows, in the frequency domain, the result of the real anti-aliasing filter. This curve shows aliasing at  801 . 
     FIG. 9 shows more detail of box  112 . Input  901  comes from a signal-to-noise calculator. The preferred type of noise detection is disclosed in U.S. Pat. No. 6,359,658, entitled “Subjective Noise Measurement on Active Video Signal”, incorporated herein by reference. Input  908  comes from box  111 . Both the edge detector  902  and the peaking unit  904  use both inputs  908  and  901 . The edge detector  902  will be further described below. The peaking unit  904  can be any conventional peaking unit, such as the one shown in European patent document EP 0257129. The peaking unit will be shut off if an edge is detected. The sub-pixel level Luminance Transient Improvement (“LTI”)  903  takes inputs from both the edge detector  902  and box  111 . The unit  903  is described in a patent application by the same inventors as the present application and entitled “System and Method for Improving the Sharpness of a Video Image” (ID 701025), filed concurrently herewith, and which is incorporated herein by reference. The outputs  906  and  907  are both in YUV 4:2:2 format. These outputs are then added by adder  905 . The output signal from the adder  905  is added to the input signal  908  from box  111  by adder  909 . The output of adder 909 goes to box  113  from FIG.  1 . Signals on line  908  are in YUV 4:2:2 format. 
     Conceptually, the device of FIG. 9 combines a linear technique, i.e. peaking, with a non-linear technique, i.e. LTI, to improve interpolation results. 
     The peaking unit  904  enhances high frequency content by adding a high-passed signal to the original signal. Peaking has strengths and weaknesses as an image processing technique. After peaking, the amplitudes of the transients are increased; but the peaking process can easily be overdone, resulting in large rims about big transients. Moreover, peaking lacks robustness on noisy images. “Intelligent” control, required to avoid artifacts, may be attempted using a more complex peaking algorithm; however, the linear nature of peaking means that even with these more complex algorithms new frequencies cannot be introduced. 
     LTI shifts pixels towards the edge center. Edge detection decides which pixels need to be shifted using LTI. Therefore higher than normal frequencies are generated around the edge, and the slope of the transient is increased. LTI is robust in the presence of noise, when edge detection is reliable. Preferably edge detection is related to the signal-to-noise ratio of the input video signal. When edge detection is dependent on signal-to-noise ratio, then the control of the output will also be dependent on signal-to-noise ratio. Preferably, also, the subjective effect of LTI is not as strong as that of peaking, when the amplitude of a transient is small. 
     In the preferred embodiment, peaking and LTI are combined such that peaking works on small transients, while LTI works on large transients. In other words, for small transients, the amplitude is increased, whereas for large transients, the slope is increased. This technique makes a distinction between a soft edge, to which LTI will not be applied, and a sharper edge, to which LTI will be applied. 
     Generally, overflow/underflow problems that yield annoying artifacts only occur where there are large transients, i.e. on edges. Accordingly, underflow/overflow does not need to be prevented in the peaking unit, saving considerable circuitry or computing time there. 
     Though most of this disclosure is related to one dimensional image processing, effected in two passes, one horizontal and one vertical; 2D techniques may be used as well, for instance, 2D peaking may be used on texture parts of the image. 
     FIG. 10 illustrates flow of the preferred edge-detection procedure for use in box  902 ; however, other edge detectors may be designed by those of ordinary skill in the art for the purpose of input to the unit  903 . The procedure of FIG. 10 may be performed in any type of processor, for instance, either by hard-wired hardware or by a programmed multi-purpose processor. For instance, boxes  1003 - 1005  may each be realized by an independent special purpose circuit. 
     In a two dimensional image, edge detection in accordance with FIG. 10 is preferably performed in each dimension separately. In other words, the flow chart of FIG. 10 is performed twice. This is preferred for two reasons. First, the eye is typically more sensitive to lines and edges in horizontal and vertical directions. Second, a diagonal edge appears as an edge in both horizontal and vertical directions. 
     The dimensions may be scanned in either order, vertical first or horizontal first. During the vertical scan, only horizontal edges will be detected and marked as edges. During the horizontal scan, only the vertical edges will be detected and marked as edges. 
     Box  1001  shows a request for input of a new pixel. At box  1002 , a test is performed to see if the last pixel has been passed. If so, an edge linking procedure  1007  is begun. If not, at  1003 , it is tested whether a low-pass second derivative crosses zero. If the result of the test of box  1003  is positive, an edge has been detected on the pixel level and control passes to  1004 . If not, control passes to box  1001 . 
     At  1004 , it is determined whether a low-pass first derivative is larger than a value referred to as “Threshold 1 ”. Where the maximum value of the derivative is 255, the preferred value for Threshold 1  is 20, though those of ordinary skill in the art may use other values in order to achieve various desired effects. If the test of box  1004  is not met, this is a false edge caused by noise, in which case, control returns to  1001 . If the test is met, the possibility of a false edge has been eliminated and control passes to box  1005 . 
     At  1005 , it is tested whether a low-pass third derivative is larger than the product of a value referred to as “THRESHOLD 2 ” and the low-pass first derivative. The preferred value for THRESHOLD 2  is 0.45, though again those of ordinary skill in the art may use other values in order to achieve other desired effects. In general, changing the values of THRESHOLD 1  and THRESHOLD 2  will determine whether the picture is sharper or fuzzier. If the two thresholds are low, in general, it makes the image sharper. Which is desired may be a matter of personal taste. If the test of box  1005  is not met, the putative edge is too soft to apply LTI to, and control is returned to box  1001 . If so, the pixel is marked as an edge at  1006 . 
     Determining Whether the Low-pass Second Derivative Crosses Zero 
     The operations to be performed in box  1003  will now be described. A low-pass second derivative can be defined according to the following equation:                SD                   (   n   )            =   Δ            L                   (     n   -   m     )       -     2   ×   L                   (   n   )       +     L                   (     n   +   m     )                 (   1   )                         
     Where 
     n is an integer variable denoting a present pixel 
     m is an integer variable related to the up-scaling factor. For an upscale factor of 2, the typical value of m will be four, but those of ordinary skill in the art may derive other values depending on what works best experimentally 
     L(n) is the luminance value at pixel n 
     SD(n) is the low-pass second derivative at pixel n          =   Δ          means                   “     is                 defined                 as     ”                       
     The Laplace operator can be defined as follows:                S                   (   n   )            =   Δ            L                   (     n   -   1     )       -     2   ×   L                   (   n   )       +     L                   (     n   +   1     )                 (   2   )                         
     where the function S(n) represents the Laplace transform of pixel n. More about the Laplace operator can be found in U.S. Pat. No. 5,852,470, especially in FIGS. 14A-E and the accompanying text, which patent is incorporated herein by reference. 
     The low-pass second derivative is preferred in order to eliminate the effects of aliasing, as shown in FIG. 8 b . The low-pass second derivative according to the preferred embodiment uses the vector [−1, 0, 0, 0, 2, 0, 0, 0, −1], in contrast with the vector [−1, 2, −1] that is more commonly used for a second derivative, i.e. Laplace operator. This preferred vector can be derived by convolution of a low pass filter, [1, 2, 3, 4, 3, 2, 1] with the traditional Laplace operator. 
     FIG. 11 shows the frequency spectrum of the traditional Laplace operator and the low-pass second derivative, when combined with the anti-aliasing filter, in the frequency domain. The low-pass second derivative yields the lower frequency curve. 
     Therefore, the low-pass second derivative can also be calculated in accordance with the following equation: 
     
       
           SD ( n )=[1, 2 , . . . , m , . . . , 2, 1 ]{circumflex over (X)}S ( n )  (3) 
       
     
     Where [1, 2, . . . , m, . . . ,2, 1] is the impulse response of a low pass filter. Equation (3) is thus equivalent to equation (1). 
     The advantage of using the low-pass second derivative, as compared with the Laplace operator alone, is increased robustness in the face of noise and aliasing. Zero crossing is preferably determined where 
     
       
           SD ( n )· SD ( n −1)&lt;0  (4) 
       
     
     This zero crossing calculation identifies candidate edge pixels. 
     Determining Whether the Low-pass First Derivative is Larger than THRESHOLD 1   
     The operations to be performed within box  1004  will now be described. 
     The low-pass first derivative is preferably determined in accordance with the following equation:                FD                   (   n   )            =   Δ            L                   (     n   -   m     )       -     L                   (   n   )                 (   5   )                         
     where the function FD(n) represents the low-pass first derivative at pixel n. 
     The gradient operator can be calculated as follows:                F                   (   n   )            =   Δ            L                   (     n   -   1     )       -     L                   (   n   )                 (   6   )                         
     Then, analogously with the previous calculation for the low-pass second derivative, the low-pass first derivative can be calculated as follows: 
     
       
           FD ( n )=[1, 1, 1, . . . , 1 ]{circumflex over (X)}F ( n )  (7) 
       
     
     where [1, 1, 1, . . . , 1] is an m-bit vector and is the impulse response of a low-pass filter. Thus both the low-pass first derivative and the low-pass second derivative are based on a low pass version of standard operators. The vector in each case is chosen to simplify the coefficients resulting after convolution. Those of ordinary skill in the art may devise other vectors to achieve a low pass effect for these derivatives. 
     If 
     
       
         | FD ( n )|&gt;THRESHOLD 1   (8) 
       
     
     then the candidate pixel is retained. This calculation helps distinguish noise from actual edges. 
     Determining Whether the Low-pass Third Derivative is Larger than THRESHOLD 2  Multiplied by the Low-pass First Derivative 
     The operations within box  1005  will now be described. This box effects what will by referred to herein as an “edge frequency calculation”. This calculation determines the slope of the edge. In video signals, some edges have steep slopes, while some edges have more gradual slopes. Generally, one does not want to sharpen the edges that have more gradual slope. These edges may have been deliberately softened, for instance to give a face a soft or smooth look. The soft edges will generally have a lower frequency content. Accordingly, edges with a higher frequency content should be sharpened. 
     Since it is difficult to locate the start or end of an edge, it is not accurate to calculate the edge slope from the first derivative alone. Accordingly, it is proposed to use the ratio between the low-pass third derivative and the low-pass first derivative as an indication of edge frequency. 
     The low-pass third derivative can be calculated from the low-pass second derivative in accordance with the following equation: 
     
       
         
           
             
               
                 
                   
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     where the function TD(n) is the low-pass third derivative at pixel n. Since a low-pass second derivative is used, TD will be a low-pass third derivative. 
     The following calculation may performed:                  TD        (   n   )         FD        (   n   )         &gt;   THESHOLD2           (   10   )                         
     If the condition is true, then the edge frequency is high, so the edge becomes a candidate for sharpness enhancement. Since division is fairly expensive, from an implementation point of view, the calculation of equation 10 is preferably expressed as a test of whether the low-pass third derivative is greater than the product of the low-pass first derivative and THRESHOLD 2 . This product calculation is fully equivalent to performing the ratio calculation of equation (10). Accordingly, herein, when it is stated that the ratio of the third to the first derivative is greater than a threshold, this will include the possibility that the third derivative is compared to the product of the threshold and the first derivative. When the term derivative is used herein without the term “low-pass”, the intention is to include the low-pass version as a possible embodiment. 
     Let us call the ratio between the low-pass third and low-pass first derivative α. FIGS. 12A-C show the effect on edge detection for different values of α. FIG. 12A is for α=0.3; FIG. 12B is for α=0.5; and FIG. 12C is for α=1.0. Each of these figures shows a pattern of increasing frequency. The small arrows  1201 ,  1202 ,  1203  below the figures show where edges begin to be detected. In each case, all edges at and to the right of the arrow are detected as edges, and values to the left are considered soft edges. LTI is not applied to the soft edges, only to the edges at or to the right of the arrow. 
     Edge Linking 
     FIGS. 13A-F and  14 A-F illustrate the preferred edge linking rules for the vertical and horizontal scans, respectively. Those of ordinary skill in the art may also devise other types of edge linking techniques for use with the edge detection disclosed above. 
     The rules illustrated in the figures depart from the assumption that isolated pixels indicated as edges are probably not really edges. Edges typically extend over several pixels. 
     The rules are illustrated as considering a neighborhood of a present pixel. The present pixel is marked with an ‘x’ in each case. The neighborhood has been chosen to be 3×3, for simplicity, but those of ordinary skill in the art may use other size neighborhoods, to give better results. Those of ordinary skill in the art may design neighborhoods that are not square. The edge linking rules for neighborhoods of different sizes and shapes will be different from those illustrated here for the 3×3 case. 
     In the figures, the darkest shaded squares represent pixels that have been definitely established to be edges. The less darkly shaded squares represent pixels that are to be ignored with respect to the rule in question. The rules of FIGS. 13A-D and  14 A-D relate to reconstruction of lines with breaks in them, while the rules of FIGS. 13E-F and  14 E-F relate to elimination of false edges. 
     FIGS. 13A&amp;B illustrate rule 1 for the vertical edge detection scan. FIG. 13A shows a situation where the upper left and upper right squares are known to be edges. In this situation, in accordance with FIG. 13B, the edge linker should mark the entire top row of pixels as an edge and the present pixel as not an edge. 
     FIGS. 13C&amp;D illustrate rule 2 for the vertical edge detection scan. FIG. 13C, shows the inverted situation from FIG.  13 A. Now the bottom left and bottom right pixels have been identified as edges. In this situation, in accordance with FIG. 13D, the edge linking module should mark the entire bottom row of pixels as an edge and the present pixel as not an edge. 
     FIGS. 13E&amp;F illustrate rule 3 for the vertical edge detection scan. FIG. 13E illustrates a situation in which all of the pixels surrounding the present pixel are known not to be edges. In such a case, the edge linker marks the present pixel to be not an edge. 
     FIGS. 14 A&amp;B illustrate rule 1 for the horizontal edge detection scan. This is a 90 degree rotation of the rules of  13 A-D. In FIG. 14A, the top and bottom left pixels are marked as edges. FIG. 14B shows that as a result of edge linking, the entire left column of pixels is marked as an edge, while the present pixel is marked as not being an edge. 
     FIGS. 14 C&amp;D illustrate rule 2 for the horizontal edge detection scan. In FIG. 14C, the top and bottom right pixels are marked as edges. FIG. 14D shows the resulting action of the edge linker. In FIG. 14D, the entire right column of pixels is marked as an edge and the center pixel is marked as not an edge. 
     FIGS. 14E&amp;F show rule 3 for the horizontal scan. Rule 3 for the horizontal scan is the same as rule 3 for the vertical scan. 
     From reading the present disclosure, other modifications will be apparent to persons skilled in the art. Such modifications may involve other features that are already known in the design, manufacture and use of image enhancement techniques and which may be used instead of or in addition to features already described herein. Especially, where hardware embodiments are shown, software may be substituted; and, where software embodiments are shown, hardware may be substituted. Although claims have been formulated in this application to particular combinations of features, it should be understood that the scope of the disclosure of the present application also includes any novel feature or novel combination of features disclosed herein either explicitly or implicitly or any generalization thereof, whether or not it mitigates any or all of the same technical problems as does the present invention. The applicants hereby give notice that new claims may be formulated to such features during the prosecution of the present application or any further application derived therefrom. 
     The word “comprising”, “comprise”, or “comprises” as used herein should not be viewed as excluding additional elements. The singular article “a” or “an” as used herein should not be viewed as excluding a plurality of elements.