BLOCK MATCHING MOTION ESTIMATION

Abstract: A block matching motion estimation algorithm uses reduced precision data when comparing blocks in the pictures to be compressed with blocks in a reference picture. This yields a plurality of best match candidates (66). The average (70) of the best match candidates (66), excluding those candidates (68) which fall outside a predetermined cluster boundary (74), is determined and then a second search is performed focused on this average (70). In this way, the amount of hardware for the block matching algorithm is decreased and the speed of its operation is enhanced.

Description: Block matching motion estimation. The invention relates to the field of digital video compression and specifically to a method for producing a motion vector while reducing the complexity and increasing the speed of the required computations. Briefly, compression algorithms recognize that not every bit of every frame of data need be encoded and transmitted. Samples of data are generally similar enough to each other so that one sample could be used as an estimate of the next sample. These similarities can be further broken down into spatial and temporal relations. Spatial similarities assume that for a given frame of data, information in one portion of the frame will be similar to other information in the same frame. Temporal similarities assume that consecutive frames will generally be similar. This means a frame can basically be represented by a frame which came immediately before it. The temporal similarities are determined using motion estimation algorithms. There are many types of motion estimation algorithms. These include: the block-based method, pixel-based recursion, and feature based. The block based method, generally referred to as a block matching algorithm, has been adopted by several video compression standards including H.261 and MPEG (Moving Picture Experts Group), and is the focus of the invention. In the block matching algorithm, a field or frame (collectively referred to hereinafter as a "picture") which is to be motion compressed is compared to a previously encoded picture which is not motion compressed. The comparison is a correlation of the luminance values between the pictures. The non-motion compressed pictures are called I- pictures (or intra-pictures). They are compressed using only spatial techniques such as a discrete cosine transform. During compression, the first picture to be encoded, which is compared to the I-picture, is called a P-picture (predictive). The P-picture or I-picture could also be used as a reference for encoding a B-picture (bi-directional, prediction can come from prior or subsequent I- or P-pictures). Therefore the terms "reference picture" and "coded picture" will be used throughout the specification to avoid limitation in scope. All of the pictures (both coded and reference) are divided into a plurality of picture elements ("pixels" or "pels"). A macroblock is defined as a rectangular block having 16 by 16 pixels and is the foundation of block matching compression techniques. For each macroblock of coded picture, a search is performed in a corresponding search area of a reference picture for the macroblock yielding the best match. A best match motion vector is ascertained which starts at the origin of the coded macroblock in the reference picture and points to the coded macroblock. The search area is basically a window of the reference picture which generally corresponds spatially to the macroblock of the coded picture. The best match is usually determined by subtracting the corresponding pixels in each macroblock from one another, squaring the result, and then adding up all the squared subtracted values. This is known as the Mean Square Error and is mathematically expressed as: MSE(i, j) = - I ∑ ∑(5 / (m,n) - 5 ( . 1 (m - ϊ -,n - )) 2 ,-w < (i, j) ≤ w N =l π=l where NxN is a rectangular block size, w is the search range, S t is the block to be motion compressed in the coded picture, S t-1 is the reference picture, and i and j represent the horizontal and vertical shift, respectively, of the reference macroblock in the reference picture. This produces the most accurate comparison because any differences between the compared blocks are squared thereby emphasizing larger differences. As can easily be discerned, this comparison is an expensive process because it requires a multiplier which is not cheaply realized in silicon technologies. Moreover, this comparison is performed for each possible macroblock location in the search range. Considering the large amount of computations required, and the need for a multiplier, this comparison is generally not used in real world applications, but is useful in computer simulations. One example of a more practical comparison is the Mean Average Error (MAE). This computation is similar to the MSE but takes the absolute value of the results instead of squaring them. The Mean Average Error is thus defined as: MAE(i, j) = - - ∑ ∑ \S, (m, ή) - S,_, (m - i, n - j)\ -w ≤ (i, j) < w N m=l n=\ However, even this comparison causes problems because the search area chosen could be quite large and each computation requires many circuits. One prior art solution utilizing a hierarchical approach, decimates the reference picture thereby reducing the number of pixels which need to be analyzed. The decimation process generally includes taking a specified number of pixels, usually 4, in the horizontal and vertical direction, and replacing them with their average value. This yields a picture with 16 times less information which can be more easily processed but with decreased precision. The decimated image is searched and a relatively imprecise motion vector is determined. Then, based on the imprecise motion vector, a second detailed search is performed on the full image centered around the imprecise motion vector but with a smaller search range. However, even during the decimated search portion, this technique still requires an enormous amount of computations to be performed for each iteration of computing the MSE or MAE. This hierarchical technique is thus costly because a large amount of hardware is still necessary due to pixels with full precision accuracy being analyzed even within the decimated image. Therefore, there exists a need for a motion estimation technique which is less expensive to implement and which requires less processing time than the techniques of the prior art. It is an object of the invention to produce a method of determining a motion vector which faster and less expensive than prior art techniques. To this end, the invention provides a method, a computer-readable storage medium, and a transmitter as defined in the independent claims. The dependent claims define advantageous embodiments. One aspect of the invention is a method for locating a first block in a first picture which corresponds to a second block in a second picture. The method includes dividing the first and second pictures into a plurality of pixels, each of the pixels having a value with a first precision, and defining a first search area within the first picture. The method further includes reducing the precision of the values of the pixels in the first and second pictures thereby producing pixels with a second precision. The method still further includes comparing the pixels with the second precision in the second block with pixels with the second precision in all blocks disposed within the first search area, thereby producing a plurality of best match candidates, and averaging the best match candidates to produce the first block. This aspect, like the others, yields a technique of finding a motion vector which is faster and less costly than prior art techniques. This is accomplished through the comparison of reduced precision pixels. These aspects, as well as others, will become more apparent from the following description read in conjunction with the accompanying drawings where like reference numerals are intended to designate the same elements. Fig. 1 is a diagram showing a transmission system for use with the invention; Fig. 2 is a diagram showing a storage medium which uses the invention to store data; Fig. 3 is a diagram of an exemplary sequence of I- and P-, and B-pictures; Fig. 4 is a diagram juxtaposing a reference picture and a coded picture thereby indicating how a search is performed for a block in the reference picture which corresponds to a block in the coded picture; Fig. 5 is a diagram showing how a plurality of best match results are used to produce a computed best match; Fig. 6 is a diagram showing how differing precision levels of data produce extrapolated contours which center around an actual best match macroblock; and Fig. 7 is a flow chart summarizing the invention. In Fig. 1 an encoder 50 encodes and compresses a stream of data which is sent via a transmission medium 52 to a receiver 54. Transmission medium 52 could be, for example, coaxial wire, air (in the case of a terrestrial transmission), or any other suitable transmission medium. Alternatively, as is shown in Fig. 2, the invention could be used to simply encode and compress data using an encoder 56, onto a recording medium 58 or directly to a display 60 through MPEG decoder 59. Any conventional MPEG decoder can be used. Fig. 3 shows a typical series of I- P-, and B-pictures which are to be encoded. Each I-picture is encoded independently of the other pictures; that is, only spatial ("intra- picture") compression is performed on these pictures. A P-picture is motion compressed based upon a comparison with an I-picture. A B-picture is motion compressed based on a comparison with either I- or P-pictures. As shown in Fig. 4, each of these coded pictures are divided into a plurality of macroblocks which are defined as a square of 16x16 pixels. Similarly, the reference picture used for comparison is also divided into a plurality of macroblocks. For each macroblock to be encoded 64 in the coded picture, a search is performed in the reference picture for the block which most corresponds to macroblock to be encoded 64. This search is performed in a search area 62 for a best match macroblock 64B. Search area 62 is divided into a plurality of overlapping macroblocks. For example, a first macroblock is examined and then the system moves one row of pixels to create the origin of a new macroblock to be examined. These macroblocks are shown as 64^ where m stands for match and i is the i-th number of the macroblock. The idea behind this search is that since the reference picture was already transmitted, we could use the data from the reference picture, pick the best match macroblock 64B in the reference picture which most corresponds to the macroblock to be encoded 64, create a motion vector 66 which indicates any translational displacement between best match macroblock 64B and macroblock to be encoded 64, and then transmit any luminance value differences between macroblock to be encoded 64 and best match macroblock 64B. The invention is based on the fact that the increased hardware and decreased speed existing in the prior art techniques are based upon the fact that the entire luminance value, usually represented by 8 bits, is used in the comparison between the coded and reference pictures. The invention thus rounds all of the luminance values to a smaller number of bits - for example 4 - and then the search is performed as is known in the art except that a plurality of best match candidates is ascertained. In MAE and MSE calculations, many adders are required. Reducing the number of bits involved in the comparisons, increases the speed in which the adders could operate. This clearly results in a less precise search but that may be sufficient for applications where a smaller ratio of data compression is acceptable (i.e. a camcorder display). For applications which do require a more precise estimation, the invention further finds a group of best matches. This is distinguished from prior art techniques which only find one best matching block and store it. There is a decrease in precision by rounding the luminance values as explained above and so a group of best matching blocks is stored in a first step of the block matching process. Statistically, this group, or cluster, will be centered around the macroblock with the best match. The rounding of the luminance values can be performed in many known ways. For example, if one were to round to 4 bits from 8, the least significant nibble would be rounded off. If a luminance value is represented by the 8bit value 5Xhe x , if was equal to 0 to 7, the number would be rounded to 50 hex which would be quantized to a level 5. Similarly, if X was between 8 and 15, then the number would be rounded to 60 hex or quantized to a level 6. Referring to Fig. 5, ten best match candidates are ascertained. (Clearly, 10 is an arbitrary number and more or less candidates could be used.) A cluster boundary qualifier box 74 is formed around a plurality of the best match candidates which are within a predefined proximity of each other thereby producing excluded match results 68 which fall outside the boundary and including a number of included match results 66 falling inside the boundary. The cluster boundary qualifier box 74 can be formed using many techniques. For example, the center formed by a majority of the best match candidates can be ascertained and then a distance from that center chosen dictating a side of the box. An average of all the included match results 66 is taken to produce a computed best match 70. As can be discerned, this computed best match 70, even though produced from less precise data, is close to the actual best match 72. This phenomena of producing a computed best match 70 near an actual best match 72 even though less precise data is used, can be explained with reference to Fig. 6. In Fig. 6, contours are shown representing the connected extrapolation of included best match results for five distinct luminance value representations. As is shown, if an 8 bit representation is used for the luminance value, the best match results will statistically be closer to actual best match 72 and will be located within a contour which has a smaller area than a representation of 7 or fewer bits. This is because all of these included best match results fall close to actual best match 72. However, regardless of how many bits are used, if one takes an average of the results, one would still come up with a value which is close to the actual best match 72. The phenomena could be analogized to a game of darts Those with the greatest skill in the game are like the 8-bit version and will produce results all near the actual best match 72. Similarly, those who are not as skilled, like the 4 bit version, will produce results which are generally further away from the actual best match 72. However, it is clear, regardless of the choice of skill (or data representation), what was the actual target (or actual best match). Referring back to Fig. 5, once the computed best match 70 is determined, a second search can be performed on full 8-bit luminance data but for a much smaller search area centered around computed best match 70. For example, a square with a side of 2-4 pixels surrounding computed best match 70 could be defined, and then the search area would include the macroblocks with origins corresponding to each of the pixels in the square. As stated above, this second search does not have to be performed for applications which do not require as much quality. The invention can be summarized with reference to Fig. 7. In step S2, the picture to be encoded and the reference picture are divided into a plurality of pixels. In step S4, a first search area is defined in the reference picture. The precision of all of the pixels is reduced by rounding in step S6. In step S8, the reduced precision pixels are compared to produce best match candidates. To improve precision, the best match candidates which do not fall within close proximity with the rest of the group of best match candidates can be optionally removed from consideration in step S10. In step S12, the average of the remaining best match candidates is taken to produce a computed best match. A second search can be performed around the computed best match in step S14 and a second comparison is performed on full precision pixels in step S16. Therefore, by performing a first search on less precise data, the hardware necessary to perform the motion vector computation is reduced and the time required to find the motion vector is decreased. The smaller bit arithmetic operation leads to faster hardware implementation. This technique alone is sufficient for situations where smaller compression ratios are acceptable. However, by storing a plurality of included best match results and then performing a second search based upon the average of the first best match results, the precision lost by analyzing reduced precision data is returned. Having described the preferred embodiments it should be made apparent that various changes could be made without departing from the scope and spirit of the invention which is defined more clearly in the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps other than those listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In a device claim enumerating several means, several of these means can be embodied by one and the same item of hardware. A preferred embodiment of the invention can be summarized as follows. A block matching motion estimation algorithm uses reduced precision data when comparing blocks in the pictures to be compressed with blocks in a reference picture. This yields a plurality of best match candidates (66). The average (70) of the best match candidates (66), excluding those candidates (68) which fall outside a predetermined cluster boundary (74), is determined and then a second search is performed focused on this average (70). In this way, the amount of hardware for the block matching algorithm is decreased and the speed of its operation is enhanced.

Claims: CLAIMS: 1. A method for locating a first block (64B) in a first picture (I) which corresponds to a second block (64) in a second picture (P), said method comprising: dividing (S2) said first and second pictures into a plurality of pixels, each of said pixels having a value with a first precision; defining (S4) a first search area (62) within said first picture (I); reducing (S6) the precision of the values of said pixels in said first and second pictures thereby producing pixels with a second precision; comparing (S8) the pixels with said second precision in said second block (64) with pixels with said second precision in all blocks (64^) disposed within said first search area (62), thereby producing a plurality of best match candidates (66); and averaging (S12) said best match candidates (66) to produce said first block (64B). 2. The method as claimed in claim 1 further comprising the steps of: defining (S14), after said step of averaging (S12), a second search area based upon said first block (64B); and comparing (SI 6) the pixels with said first precision in said second block (64) with pixels with said first precision in all blocks disposed within said second search area, thereby producing a more accurate first block (64B). 3. The method as claimed in claim 1 further comprising: creating (S10) a cluster boundary qualifier (74) enclosing a plurality of said best match candidates (66) which are located within a predetermined proximity of each other; and excluding (S10) from consideration any best match candidates (68) which are not included in said cluster boundary qualifier (74). 4. The method as claimed in claim 1 wherein said step of reducing (S6) the precision comprises rounding said value of all of said pixels. 5. A computer-readable storage medium (58) containing computer-readable instructions for performing the method as claimed in claim 1. 6. A transmitter (50) for transmitting data from a transmitter (50) to a receiver (54), said data including a plurality of pictures of at least a first and second type (I,P), said transmitter (50) comprising means for locating a first block (64B) in a picture of said first type (I), which corresponds to a second block (64) in a picture of said second type (P), said means for locating comprising: means for dividing (S2) said pictures of first and second type (I,P) into a plurality of pixels, each of said pixels having a value with a first precision; means for dividing said pictures of first and second type into a plurality of blocks, said blocks each including a plurality of said pixels; means for defining (S4) a first search area (62) within said pictures of said first type (I); means for reducing (S6) the precision of the values of said pixels in said pictures of a first and second type (I,P) thereby producing pixels with a second precision; means for comparing (S8) the pixels with said second precision in said second block (64) in a picture of said second type (P) with pixels with said second precision in all blocks (64 m i) located within said first search area (62), thereby producing a plurality of best match candidates (66); and means for averaging (SI 2) said best match candidates (66) to produce said first block (64B). 7. The transmitter (50) as claimed in claim 6 further comprising: 5. means for defining (SI 4) a second search area based upon computed best match (70); and means for comparing (SI 6) the pixels with said first precision in said first block (64) with pixels with said first precision in all blocks (64^) disposed within said second search area, thereby producing a more accurate computed best match result. 0 8. The transmitter (50) as claimed in claim 6 further comprising: means for creating (S10) a cluster boundary qualifier (74) enclosing a plurality of said best match candidates (66) which are disposed within a predetermined proximity of each other; and 5 means for excluding (S10) from consideration any best match candidates (68) which are not included in said cluster boundary qualifier (74). 9. The transmitter (50) as claimed in claim 6, said means for reducing (S6) the precision comprises means for rounding said value of all of said pixels.

Inventors: BAKHMUTSKY MICHAEL, BAKHMUTSKY, MICHAEL
Applicants: KONINKL PHILIPS ELECTRONICS NV, KONINKLIJKE PHILIPS ELECTRONICS N.V.
Application year: 2000
Main classification: H04N7
IPC codes: G06T7, H04N7