Abstract:
A method and apparatus evaluates quantization matrices used in video codec systems. Two primary factors are considered in making these estimates. The first is the human visual system contrast sensitivity function. This function measures how well a quantization matrix fits human visual characteristics. The second factor is a typical viewing setting, such as a range of typical viewing distances. For consumer use, the viewing range is one to four times picture height. For professional use, it is assumed the viewing range is one-half to three times picture height. The quantization matrix used in a video codec system defines the quantization step for different frequency bands. This quantization step is essentially equivalent to the allowable error in a frequency band. The present invention evaluates the quantization matrix for its effectiveness in hiding distortion errors. By using this evaluation scheme, the quantization matrix can be modified as needed, and the overall performance of the quantization matrix in a video codec system is improved substantially.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS  
       [0001]     This application claims the benefit of provisional Patent Application No. 60/540,437 filed Jan. 30, 2004, for A Method For Maximizing The Effectiveness Of Quantization Matrices In Video Codec Systems, and hereby incorporates by reference all the contents thereof. 
     
    
     BACKGROUND OF THE INVENTION  
       [0002]     1. Field of the Invention  
         [0003]     The present invention relates generally to improvements in video codec systems, and more particularly pertains to new and improved quantization procedures in video codec systems.  
         [0004]     2. Description of Related Art  
         [0005]     The quantization process is one of the most important processes in video coding systems. Traditionally, quantization involves two major schemes, uniform quantization and use of a quantization matrix. The quantization matrix scheme has been implemented to provide a picture coding system that exploits non-linear human visual perception characteristics. The popularity of quantization matrices has caused them to be utilized in several international video coding standards such as MPEG-2 and MPEG-4. There are still coding standards that use uniform quantization schemes such as H.263 and MPEG-4AVC.  
         [0006]     When utilizing the quantization matrix in video codec systems, it is desirable to utilize a system which has the flexibility of using the most appropriate quantization matrix, containing different quantization values or different dimensions, such as 4×4 and 8×8, or different quantization schemes for encoded luminance (luma) and color (chroma) information. To provide this kind of flexibility, the system must be able to evaluate and make decisions as to what matrix to use. The evaluation, for example, would be for the purpose of achieving the same subjective picture quality when both an 8×8 and 4×4 quantization matrix is used within the same picture. Such evaluation could also determine whether different quantization matrices could be used for the luma and chroma in the same transform block.  
         [0007]     Prior to the present invention, there has been no process available for determining which quantization matrix would be most effective in a codec system to provide the best subjective picture quality. The present invention provides a technique for evaluating a quantization matrix, for measuring its overall performance in the codec system, for the purpose of obtaining the best subjective picture quality.  
       SUMMARY OF THE INVENTION  
       [0008]     A method and apparatus for an effective control of quantization process in a lossy moving picture compression that converts received pictures array matrix data structures into bit stream data blocks. In the quantization process, Picture Quality Level is calculated for each pair of a quantization matrix and a quantization step size. A desired Picture Quality Level is compared to a currently calculated Picture Quality Level to determine if the quantization matrix should be adjusted. The quantization matrix may be adjusted, by multiplying each element of the quantization matrix by the ratio of a desired Picture Quality Level with a currently calculated Picture Quality Level. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0009]     The exact nature of this invention, as well as its objects and advantages, will become readily appreciated upon consideration of the following detailed specification when considered in conjunction with the accompanying drawings, in which like reference numerals designate like parts throughout the figures thereof, and wherein:  
         [0010]      FIG. 1  is a block diagram of a video encoder that may utilize the present invention to its advantage.  
         [0011]      FIG. 2  is a block diagram of a video decoder that may be utilized with the video encoder of  FIG. 1 .  
         [0012]      FIG. 3  is a diagrammatic illustration of the relationship between frequency of the picture and transform coefficients in a quantization matrix.  
         [0013]      FIG. 4  is a diagrammatic illustration of a weighted quantization matrix.  
         [0014]      FIG. 5  is a diagrammatic illustration of quantization blocks of different sizes next to each other.  
         [0015]      FIG. 6  is a process flow diagram that illustrates quantization matrix evaluation and adjustment, according to the present invention.  
         [0016]      FIG. 7  is a block diagram illustrating data flow for determining quantization amounts.  
         [0017]      FIG. 8  is a wave diagram illustrating the relationship between human contrast sensitivity function (CSF) to angular frequency, which is representative of the allowable error in a quantization step. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0018]      FIG. 1  illustrates a video encoder  100  that utilizes the present application. The picture sequence input  113  is a series of pictures comprised of data structures that describe the pixel values at each pixel of the picture. As is well known, there can be several numbers associated with each pixel. These numbers are, in turn, associated with the intensity of brightness of a certain colored component at the location of that pixel. Typically, a color display combines the brightness of all the colored components to produce the actual color at the location of the pixel.  
         [0019]     The output of the video encoder  100  is a plurality of bit streams such as video stream (VS)  123 , motion vectors (MV)  125 , and quantization matrices QM ( 129 ). These data streams are combined together to produce an output that is a series of bits, a bit stream.  
         [0020]     The pixel values received by the encoder  100  at its input  113  are supplied to transform circuitry  101  which executes a well understood mathematical conversion that transforms the input picture array into a transform coefficient array. This transform coefficient array is supplied to a quantization circuit  102 , which executes a scaling operation performed by multiplying each coefficient of the transform coefficient array by a small number and dividing by a larger number. The output  129  of the quantization circuit  102  is provided as an input to the decoder  140 , as an input  131  to variable length coding circuit  103  and as an input to inverse quantization circuit  105 . The variable length coding circuit generates the video stream (VS)  123 . The inverse operation of the quantization function of quantization circuitry  102 . The inverse quantization circuit  105  generates an output of inverse quantization circuit  105  is supplied to an inverse transform circuit  107  which performs a mathematical conversion, converting the transform coefficient array back to a picture array, called the decoded picture. The decoded picture is supplied to a picture store  109 . The picture store  109  supplies picture arrays by way of connection  121  to motion block estimation circuit  111 , which detects blocks of picture areas with closest fit to the block of pictures being encoded. An output  125  of motion block estimation circuit  111  is motion vector (MV)  125  which becomes part of the bit stream.  
         [0021]     Switch  117  selectively supplies information from motion block estimation circuitry  111  to be combined with the data structure representing the series of pictures received at the input  113  to a summing circuit  99 . Selector switch  137  selectively supplies a decoded picture stored in picture store  109  to be combined with a decoded picture from the inverse transform circuit  107  by summing circuit  133 .  
         [0022]     The bit stream output of the encoder  100  of  FIG. 1  comprising a video stream (VS)  123 , motion vectors (MV)  125  and quantization matrices (QM)  129  are supplied to a video decoder  140  of  FIG. 2 . The video decoder  140  produces a decoded picture, an output  151 , which is a series of pictures, each comprised of a data structure that describes the color intensity values at each pixel in the picture array. These data structures typically include the values of the color component intensities.  
         [0023]     A variable length decoding circuit  141  in the video decoder  140  receives video stream data (VS)  123  and converts the variable length code to the actual values represented by the variable length encoded data.  
         [0024]     An inverse quantization circuit  143  receives the quantization matrices (QM)  129  from the encoder  100 . The quantization matrix is essentially an array of weighting values. A quantization matrix may be assigned to a subarea of a picture or an entire picture, for example. Both the quantization matrix and the overall quantization step size determine the quantity of quantization. The inverse quantization circuit  143  performs an inverse quantization operation which uses the quantization matrix and the overall quantization step size to determine the value of the scaling factor which is multiplied with the quantized coefficients of the transform.  
         [0025]     A motion compensation circuit  146  receives the motion vectors (MV) on line  125  from the bit stream and utilizes that information to find a block of pixel values from one of the previous reference pictures stored in the referenced picture store  147 . For each picture block outputted from inverse transform circuit  145 , a corresponding motion block is determined by the motion vectors associated with that picture block. The pixel values for that motion block obtained from a reference picture are added to the outputted block which is then supplied to a display.  
         [0026]     Reference picture store  147  is essentially a memory that stores all the decoded pictures so that they can be used as reference pictures for decoding subsequently received pictures. These reference pictures are referenced by the received motion vectors to obtain the corresponding motion blocks. The K 1  switch  153  is open if a picture will not be used as a reference, and will not be supplied to reference picture store  147  over line  51 . The K 2  switch  155  will be open when the decoding process does not use any reference pictures.  
         [0027]     In order to measure the overall performance of the quantization matrices being utilized, two factors must be considered. The first is the human visual system contrast sensitivity function (CSF). This function describes how much contrast sensitivity the human vision system has at different frequency bands. The CSF measures whether a quantization matrix fits human visual characteristics. The second factor is the typical viewing setting for the target picture content. This factor must be considered because the spatial frequency of the CSF is measured in units of viewing degree as shown by viewing angle  169  in  FIG. 3 .  FIG. 3  illustrates a human eye  163  viewing a picture screen which contains picture content at different locations  165  and  167  and different frequencies.  
         [0028]     Typically consumer picture content is to be in the range of one to four times picture height. Professional picture content is assumed to be viewed in the range of one-half to three times picture height. The closer the viewing distance, the more visible distortions appear to the viewer  163 .  
         [0029]     A quantization matrix defines the quantization weights for different frequency bands (approximately). The quantization weights can be essentially determined in proportion to to the allowable error in the angular frequency band. The human vision sensitivity function CSF can be plotted against the angular frequency, producing a relationship, as shown in  FIG. 8 . The maximum frequency  225  is illustrated by a vertical line on the frequency axis. All higher frequencies are in the sub-pixel range  227 .  
         [0030]     If a quantization step is small and the visual sensitivity is low, it is likely that any distortion will be less visible.  FIG. 3  illustrates transform coefficients C(i,j)  173  and  175  in a transform block  171 . Transform coefficient  173  corresponds to a lower frequency sample. Transform coefficient  175  corresponds to a higher frequency sample  167 . As illustrated in  FIG. 3 , transform coefficient  173  C(4,3)=12, and transform coefficient  175  C(4,6)=20.  
         [0031]      FIG. 4  illustrates a quantization matrix W(i,j) which illustrates how the quantization matrix defines the quantization weighting value, whereby each weighting value is provided to adjust or refine the overall quantization step size already defined directly by the quantization step or by an index to the value of the quantization step. The quantization weighting is illustrated by the following equation: 
 Quantized ( C ( i,j ))= C ( i,j )× K /( Q _step* W ( i,j ))   1.  
         [0032]     In this equation, where K is a constant, C(i,j) is a coefficient as the result of the transform (transform coefficient) at horizontal location i and vertical location j; Q_step is a quantization step value; and W(i,j) is a weighting at horizontal location i and vertical location j.  
         [0033]     The weighted transform coefficients  183  and  185  illustrated in  FIG. 3  result from the weighting of transform coefficients C(i,j).  
         [0034]      FIG. 5  illustrates transforms of different sizes,  187 ,  189  and  191  next to each other. The quantization of an 8×8 block  187  uses an 8×8 quantization matrix. Quantization of 4×4 blocks  189  and  191  is accomplished by use of a 4×4 quantization matrix. The present invention contemplates controlling the amount of quantization in an 8×8 block so that when so needed, the amount of quantization applied to the 8×8 block is the same as the amount of quantization in a 4×4 block.  
         [0035]     In order to establish a relation between different quantization matrices, for example, a relationship between the quantized luminance information (luma) with a weighting matrix, and color information (chroma) that does not use a quantization matrix, we can define a Picture Quality Index, which is essentially a weighted sum of the quantization coefficients. This value is then used to represent the suitableness of a quantization matrix for maintaining a certain subjective picture quality.  
         [0036]     This quantization matrix Picture Quality Index (QI) is computed on the basis of the human vision contrast sensitivity function (CSF) and the purpose of the picture content, such as consumer use or professional use. If we define a quantization matrix (QM) as follows, 
 
 QM={{q   11   , q   12   , . . . q   18   }, {q   21   , q   22   , . . . q   28   }, . . . , {q   81   , q   82   , . . . q   88 }}  2. 
 
 the Picture Quality Index can be derived from a general formula of summing subjective quality distortion from different sources as follows: 
 
 QI =(( a   11   q   11 ) p +( a   12   q   12 ) p + . . . +( a   18   q   18 ) p +( a   21   q   21 ) p + . . . +( a   88   q   88 ) p ) 1/p /matrix size   3. 
 
         [0037]     The value of p in the above equation is usually between 2 or 3. For simplicity, however, we can choose to use p=1, which simplifies the equation as follows: 
 
 QI =( a   11   q   11   +a   12   q   12 )+ . . . + a   18   q   18   +a   21   q   21   + . . . +a   88   q   88 )/matrix size   4. 
 
 Matrix size in Equations 3 and 4 equals the total elements in a matrix. 
 
         [0038]     The weighting values a ij  in Equations 3 and 4 suggest different degrees of error sensitivity in visual perception. They have different values at each location of the quantization matrix. The weighting value a ij  is determined by mainly two factors. The first is the spatial frequencies corresponding to the locations of the coefficients. The second is the representative viewing conditions associated with the intended coding content.  
         [0039]     Entries of the quantization matrix corresponding to different spatial frequency components may have different values reflecting different error sensitivity and visual perception at different frequency components. In addition, each component in a quantization matrix may have different visual sensitivity when viewing is at a different distance. As stated earlier, for consumer quality video, we shall assume the distance is in a range of one to four times the picture height. For professional quality video, we shall assume the distance in the range of one-half to three times the picture height. Assuming a viewing range of one to four times picture height and an 8×8 quantization matrix, we can obtain the derived error sensitivity weighting as follows: 
 
 a   ij   =KΣ   n=1 . . . 3   CSF (tan −1 (1/((min( i,j )−1)*pict − height —in _mb_unit* n ))),  i,j&gt; 1   5. 
 
 a   11   =KΣ   n=1 . . . 3   CSF (tan −1 (1/(pict_height_in_mb_unit* n )   6. 
 
         [0040]     Assuming a 4×4 quantization matrix, the error sensitivity weighting is: 
 
 a   ij   =KΣ   n=1 . . . 3   CSF (tan −1 (1/(2*(min( i,j )−1)*pict_height_in_mb_unit* n ))),  i,j&gt; 1   7. 
 
 a   11   =KΣ   n=1 . . . 3   CSF (tan −1 (1/(2*pict_height_in_mb_unit* n )))   8. 
 
         [0041]     Because tan −1  ( ) in the above equations is typically very small, they can be simplified as the follows:  
         [0042]     For 8×8 block, 
 
 a   ij   =KΣ   n=1 . . . 3   CSF (1/((min( i,j )−1)*pict_height_in_mb_unit* n )), for  i,j&gt; 1   9. 
 
 a   11   =KΣ   n=1 . . . 3   CSF (1/(pict_height_in_mb_unit* n ))   10. 
 
         [0043]     For 4×4 block, 
 
 a   ij   =KΣ   n=1 . . . 3   CSF (1/(2*(min( i,j )−1)*pict_height_in_mb_unit* n )),  i,j&gt; 1   11. 
 
 a   11   =KΣ   n=1 . . . 3   CSF (1/(2*pict_height_in_mb_unit* n ))   12. 
 
 These weighting coefficients can be computed beforehand and specified once the quantization matrix is specified. 
 
         [0044]     The overall quantization step size can be represented by a quantization parameter (QP), essentially an index to a quantization-step table. A QP is mapped to a quantization step size value by look-up in a quantization step table. QP and the quantization step size are related monotonically, i.e., QP goes up, the quantization step size goes up. The quantization matrix must be used together with QP. For each quantization matrix, we can compute the equivalent quantization scaler of an 8×8 quantization matrix by the following general formula: 
 
 Q   mOpeq =(( a   11   q   11 ) p +( a   12   q   12 ) p + . . . +( a   18   q   18 ) p +( a   21   q   21 ) p   + . . . +a   88   q   88 ) p ) 1/p /( a   11   p   +a   12   p   + . . . +a   18   p   +a   21   p   + . . . +a   88   p ) 1/p    13. 
 
         [0045]     The equivalent quantization scaler of a quantization matrix is further used to derive the Picture Quality Level or the Equivalent Quantization Parameter for each pair of quantization matrices and a Quantization Parameter (QP). 
 
 Q =QuantizationStepSize( QP )* Q   mOpeq    14. 
 
 Where the mapping function QuantizationStepSize(QP) is the quantization step size associated with QP. 
 
         [0046]     By setting p equal to 1, Equation 13 can be simplified to: 
 
 Q   mOpeq =( a   11   q   11   +a   12   q   12   + . . . +a   18   q   18   +a   21   q   21   + . . . +a   88   q   88 )/( a   11   +a   12   + . . . +a   18   +a   21   + . . . +a   88 )   15. 
 
         [0047]     Equation 13 can also be simplified so that a ij  are either 1 or 0. The assignment of 1 and 0 to a ij  can follow the following relationship:  
         [0048]     a ij =1, for i, j satisfying i+j&lt;M. For example, M=4 for 4×4 matrix and M=7 for 8×8 matrix.  
         [0049]     In a similar manner, the equivalent quantization scaler for a 4×4 quantization matrix can be obtained. The quantization scaler can be used to look up quantization parameter equivalent value in an MPEG-4AVC specification, for example.  
         [0050]     In implementation, these values are either computed off-line and kept in tables or are computed by encoders. However, to make a customized quantization matrix and video codec default matrix work together, a customized quantization matrix transmitted to the decoder must use the same scaler as the video codec default matrix.  
         [0051]      FIG. 6  illustrates the implementation of the present invention as a picture quantization subsystem within a video codes system. Referring to the coding system of  FIG. 1 , the subsystem would operate as a subsystem within quantization circuit  102 .  
         [0052]     The picture quantization subsystem illustrated in  FIG. 6  is activated by a quantization weighting matrix (QM), or quantization parameter index (QP)  201 , or a quantization step size  202 . Thus, if QM or QP, as currently received, is different from the QM or QP of a previous transform block, or the currently received transform block size is different from a previous transform block, the quantization subsystem of  FIG. 6  is activated. If QM or QP of a chrominance block as currently received is different from QM and QP of the luminance block and different from QM and QP of the other chrominance blocks, the quantization subsystem of  FIG. 3  is also activated. The transform block size may be any one of a variety of different coding sizes. For example, 2×2, 4×4, 8×8, 8×4, 4×8, 16×16, n×m, where n and m are integers.  
         [0053]     Upon the picture quantization subsystem being activated, it is first determined whether the desired picture quality level (Q 0 ) is known ( 203 ), whether the same picture quality (Q 0 ) as the previous block should be maintained  204 , or whether the same picture quality (Q 0 ) as other chrominance of the current block should be maintained. A positive response to either one of these questions will cause the subsystem to calculate the Picture Quality Level (Q 1 ) for the combination of the quantization weighting matrix and quantization parameter to obtain the calculated Picture Quality Level (Q 1 ) for the currently received transform block  205 . The picture quality level calculation is performed according to the Equation 3 or, in simplified form, Equation 4 set forth above.  
         [0054]     Once Picture Quality Level (Q 1 ) has been determined, the ratio of the Picture Quality Level of the previous block to the calculated Picture Quality Level  
         Q   0       Q   1         
 
 is calculated. This ratio will determine ( 209 ) whether the quantization matrix QM can be adjusted. If it can be adjusted, the quantization weighting matrix QM is multiplied ( 211 ) by the ratio of  
         Q   0       Q   1         
 
 at each quantization point. 
 
         [0055]     If the quantization matrix QM cannot be adjusted, then the quantization parameter is adjusted ( 213 ) so that the new quantization step indicated by the newly adjusted quantization parameter is a product of  
           Q   0       Q   1       .