Method and apparatus for adaptive multiple-dimensional signal sequences encoding/decoding

A hybrid block matching prediction and transform based n dimensional signal sequence encoder, including an encoder motion estimator, having a cost function. A first embodiment includes an entropy-based cost function. A second embodiment includes a fast block matching search (motion estimation) method to learn the results from neighboring blocks and perform a large range search with only a small number of points to visit. A third embodiment includes a method to dynamically adjust the cost function parameters and other selected coding control parameters based on encoder outputs to optimize the quality and performance of the encoder. A fourth embodiment includes a method to enable exploring and rapid processing of fractional grid points for n dimensional block matching search (motion estimation). A fifth embodiment includes a hybrid block matching prediction and transform-based n dimensional signal sequence decoder.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This disclosure relates generally to data encoding, storage, distribution, and decoding, and more particularly but not exclusively, to n dimensional signal sequence encoding, storage, distribution, and decoding by use of an n dimensional block matching prediction method.

2. Description of the Prior Art

Digital data systems are frequently challenged to handle large quantities of data quickly enough to meet practical needs. Compact disc music requires about 1500 kilobits per second, and video needs over 200,000 kilobits per second. Both transmission and storage of data is costly, and in some cases impossible. For example, a current telephone line modem can only carry maximum bit rate at 56 kilobits per second with a perfect line condition. Although video image frames need only be handled at approximately 30 cycles per second in order to allow an observer to have the impression of continual image transmission, the data content of each image frame is very large.

Solutions to the problem of quickly handling large quantities of data have been developed by using methods of data compression, i.e., methods of reducing the quantity of bits required. Data compression has made possible technological developments including digital television, DVD movie, streaming Internet video, home digital photography and video conferencing. Compressing coders and decoders (CODECs) are used to encode (at the capturing/production side) and decode (at the receiving/reproduction side) data containing statistical redundancy (as taught in chapter 3 of the book by Iain E. G. Richardson, entitledH.264and MPEG-4Video Compression—Video Coding for Next-Generation Multimedia, published by John Wiley & Sons, Ltd., 2003, which is hereby incorporated by reference).

FIG. 1Ais a simplified block diagram for describing a prior art method for compression of image sequence data with an encoder. This system includes an input image frame4, a discrete cosine transform (DCT)14, a quantizer Q module15, a run-level coding module19, a VLC entropy coding module20, a motion estimation search (ME) module5, a difference energy based cost function calculator13, and a decoder10. The decoder10includes a motion compensation module (MC)11, an inverse DCT module T−112, and a frame buffer3for one or more frames. The motion compensation (MC) module11produces a motion compensated prediction (MCP)17, which provides one of the inputs for the frame buffer3and discrete cosine transform (DCT) module14. The motion estimation search (ME) module5outputs a motion vector MV7to both the VLC entropy coding module20and the motion compensation (MC) module11. The motion estimation search (ME) module5also receives as inputs the input image frame4, the decoded reference frame6, to search for the best location from the reference frame(s) according to certain cost function. At each search point, ME module5sends the information21about the current block and target block at the search point to the cost function calculator13, and receives the calculated cost value22from the difference energy based cost function calculator13. After the search, the motion estimation search (ME) module5outputs a motion vector MV7to both the VLC entropy coding module20and the motion compensation (MC) module11.

An image frame is input to the encoder1and the data is encoded. The encoded frame2is then transmitted to a transmission channel or storage media (not shown), and a copy of the encoded frame is decoded at the decoder10and stored as a reference frame in the frame buffer3. The next, current input image frame4is then input to the encoder1. The encoder1then searches the reference frame6for a closest match point in the reference frame for each block of a plurality of blocks that make up the current frame, using the motion estimation search (ME) module5, by calculating what is termed an “energy difference” measure, such as sum of square error or sum of absolute error between the current frame block and corresponding reference frame block located at each search point in the reference frame. The best matched location is then represented as a “motion vector”7, specifying the two dimensional location displacement of the block in the reference frame relative to the corresponding block in the current frame. Also, the difference8between the best match blocks in the reference frame and the current frame is determined. This difference8is called the “Block Prediction Difference” (BPD), or “Residue8.” Both the motion vector7and the residue8are then encoded and transmitted. The encoder1will then also decode the motion vector7and residue8and reconstruct the current frame in the same way that a decoder to which the data is sent would reconstruct the frame, and then store this frame as a reference frame. The prior art describes variations for the simplified process described inFIG. 1A.

A very significant issue is the amount of computational power that is required by the encoder in accomplishing the task of finding the best match for each block in the current frame, i.e., determining the displacement vector such that the displacement block in the reference frame is most “similar” to the current block. One prior art method of performing this task involves searching every possible block location within a pre-defined search area. This method requires astronomical computing power and is not feasible for practical real time implementation. There are many simplified methods to search a fraction of the large and complete search space to reduce the computation cost. However, the simplified methods usually have significant quality loss of the encoded video related to the “full search” encoder, and cannot provide consistent compression quality close to the “full search” encoder on all real life cases. Furthermore, all of the search methods in the prior art (including the full search method) select the best match block location primarily by calculating some type of “energy difference” measure, such as the “sum of square error”, or “sum of absolute error” between pixels of the block in the current frame and the block at each search location in the reference frame. In general, the target of the encoder is to minimize the bit rate to achieve certain acceptable quality. The currently used “energy difference” based measure does not necessarily give the best quality and bit rate in the encoded image sequence. What is needed is an improved data compression and motion estimation technology which can achieve minimized bit rate with good quality with practical and efficient computing cycles.

SUMMARY OF THE INVENTION

The present invention is a method and apparatus to effectively compress multiple-dimensional signal sequences to reduce the necessary bit-rate within certain distortion constraints. This method can be used to encode general n dimensional signal sequences, such as one-dimensional, two-dimensional, and three-dimensional signals.

A first aspect of the invention is directed to block matching based prediction in n dimensional signal sequence encoding/decoding system. The system includes: an n dimensional block scanner to scan the blocks from the input frame according to a pre-defined sequence, an n dimensional frame buffer to store at least one frame in a sequence for prediction, having at least one reference frame other than the current frame in the sequence for prediction, where n is a positive integer; an n dimensional motion estimator (or called block matching searcher), which is capable to determine the reference displacement index referring to a prediction block in the reference frames from a current block to achieve at least one target system objective; a block encoder to encode the block prediction difference (or called residue) between the current block and the prediction block and encode a reference displacement index; and a sequence encoder to wrap the encoded blocks together into a selected format.

A second aspect of the invention is directed to a block matching prediction based video signal sequence encoding/decoding system. The system includes: a video frame buffer to store at least one video frame in a sequence for prediction, having at least one reference frame other than the current frame in the sequence for prediction; a two dimensional motion estimator (or block matching searcher), which is capable to determine the reference displacement index referring to a prediction block in the reference frames from a current block to achieve at least one target system objective; a block encoder to encode the block prediction difference (residue) between the current block and the prediction block, and encode a reference displacement index; a sequence encoder to wrap the encoded blocks together into a uniquely defined format; and means for calculating entropy, where entropy measures the amount of information needed to encode the current block, calculated from at least one reference frame.

A third aspect of the invention is directed to a method to compress a sequence of n dimensional signal frames. The method includes: storing at least one n dimensional frame in a sequence for prediction, having at least one reference frame other than the current frame in the sequence for prediction, where n is a positive integer; achieving at least one target system objective by using an n dimensional motion estimator (block matching searcher), which is capable of determining the reference displacement index referring to a prediction block in the reference frames from a current block; encoding the block prediction difference between the current block and the prediction block and encoding a reference displacement index; and wrapping the encoded blocks together into a selected format.

A fourth aspect of the invention is directed to a method to conduct a motion estimation search for a current block in a current frame selected from a set of two dimensional frames. The method includes: evaluating a set of points based on a set of motion vectors of a set of neighboring blocks, for each block in a current frame, to determine a starting point, selecting a detail search starting point derived from the set of motion vectors in the neighboring blocks of each block, detail searching by detail evaluating a region in the current frame around the best starting point derived from a neighborhood exploit set, globally searching a large region in the current frame, and repeating these operations for each block in the current frame. The searching uses an entropy based cost function.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention is a method and apparatus to effectively compress multiple-dimensional signal sequences to reduce the necessary bit-rate with certain distortion constraints. This method can be used to encode general n dimensional signal sequences, such as one-dimensional, two-dimensional, and three-dimensional signals. One important application of this method is in video encoding for transmission and storage purposes. Because of this, in many of the descriptions below, the two dimensional video signal sequence compression is illustrated. However, the method and apparatus taught here can be extended to compress a general sequence of n dimensional signals, where n is a positive integer.

N Dimensional Block Matching Signal Sequence Encoding/Decoding

FIG. 1Billustrates a block diagram of one embodiment of an adaptive multi-dimensional signal sequence encoder100(also called a sequence encoder), in accordance with one embodiment of the present invention. The system can compress an n dimensional signal sequence (1504,1505, and1506inFIG. 15are some examples of n dimensional sequences). The input to the system is an n dimensional signal frame (seeFIG. 15for some examples, such as1501,1502, and1503). The frame is composed of n dimensional blocks (FIG. 17shows an example of a two-dimensional block1702in two-dimensional frame1701). This adaptive multi-dimensional signal sequence encoding system includes an n dimensional input signal frame101, a block scanner124, a current block125, a residue (or block prediction difference (BPD))114, an n dimensional coefficient de-correlation transformer T104, an optional quantizer Q module105, a run-level coder106, an entropy coder107, a motion estimator (or block matching searcher) (ME)103, an adaptive entropy based cost function estimator112, and a block decoder118.

The block decoder118includes a motion compensated predictor (MC)119, an inverse (N-D) transform module (T−1)115, and a frame buffer102of one or more frames. The motion compensated predictor (MC)119produces a motion compensated prediction (MCP)117, which provides the predicted data for the current block125. MCP117is added with the output126from the inverse N-D transform module (T−1)115to re-construct the decoded current block127, which is one of the inputs for the frame buffer102to reconstruct the previously encoded frames. This MCP117is also deducted from the current block125to generate the residue (or called block prediction difference−BPD)114, which is an input to the n dimensional coefficient de-correlating transformer T module104.

The n dimensional coefficient de-correlating transformer T module104transforms the n dimensional residue matrix114through some coefficient de-correlating transform to generate the transformed residue matrix132, which is an input to the optional quantizer Q module105to quantize the coefficients in the transformed residue matrix132to generate a quantized transformed residue matrix133, which is a sparse matrix with zeros in most of the matrix entries. The quantized transformed residue matrix133is output to a run-level coder106and the inverse N-D transformer115. The run-level coder106scans through the frequency components in the quantized transformed residue matrix133according to a selected order of the n dimensional special frequency to produce a sequence of (run, level) symbols134. This sequence of (run, level) symbols134and motion vector111are inputs to the entropy coder107to generate the entropy code135for the current block125.

The motion estimator search (ME) module103outputs a motion vector—MV (or called reference displacement index)111to both the entropy coder107and the motion compensated predictor (MC)119. The motion estimator (ME)103also receives as inputs the current (n dimensional) block125, and the decoded reference frame(s)116, to search for the motion vector MV111to minimize a selected search cost function.

At each search point, the motion estimator ME103output certain search state data129(which could include search option, MV value, current block and reference block data, and similar information) to the cost function input parameter extractor (CFIE)128, which then generates the cost function input parameters109for the adaptive cost function estimator (or simply cost function estimator CFE)112. The adaptive cost function estimator CFE112takes as inputs the parameters109extracted from CFIE128to calculate the estimated cost110required to encode the current block. The estimated cost110is sent to the motion estimator103for best motion vector selection. The cost function estimator CFE112also receives as input the error for the estimated cost113to adjust certain internal parameters or states to improve the estimation accuracy. The cost error113is derived from the actual cost108coming out from the encoder results, and the estimated cost110. The actual cost calculator ACC130takes actual cost calculator input data131(which is based on final status information from the block encoder122after completing the block encoding) to calculate the actual cost108, which is sent into the cost function estimator CFE112as feedback to improve the estimation accuracy.

As illustrated inFIG. 1B, the system keeps a set of decoded frames in the frame buffer102. The current block125from the input frame101is matched against the displaced blocks at search points in the frames (called the “reference frames”) inside the frame buffer102according to certain cost function. To better track the signal changes from frame to frame, the block size can be variable.

One embodiment of this invention uses different block sizes and shapes to match the blocks in the reference frames. Larger blocks are used to track the “movement” of larger objects, and smaller blocks are used to track the “movement” of smaller objects. This block-matching step is called “Motion Estimation” in two-dimensional video encoding cases. “Motion Estimation” and “Block Matching Search” are used interchangeably in the following descriptions.

After the best target block is found, the residue matrix114representing the difference between the current block125and the predicted block MCP117is transformed through an n dimensional coefficient de-correlating transform T module104, and in one embodiment of the invention the transformed coefficients are quantized through an quantizer Q105, then encoded together with the motion vector information.

In an alternative embodiment of the invention, the quantizer Q105can be omitted if “Lossless” encoding is required, where the encoding process is reversible, i.e., the original signal sequence can be recovered by the block decoder118with 100% accuracy.

In the video compression case, since human eyes have a limited precision, some quantization is feasible without influencing the human perception. The quantization is set up in such a way that the quantization error is not noticeable by human eyes. In a typical video compression system, the transform T is a two-dimensional Discrete Cosine Transform (DCT), (as taught in chapter 6 of a book by Iain E. G. Richardson, entitledVideo Codec Design, published by John Wiley & Sons, Ltd., 2002, which is hereby incorporated by reference).

Experimental results show that a DCT can generate close to optimal coefficient de-correlation results. However, due to computation complexity, DCT is typically applied on small fixed blocks (8×8 in the MPEG4 case, and 4×4 in the H.264 case). To achieve larger block size and save computation time, one embodiment of this invention uses the Discrete Hadamard Transform (DHT), as the coefficient de-correlating transform. DHT is not as optimal as DCT in terms of de-correlating the coefficients (according to chapter 8 of a book by Douglas F. Elliott and K Ramamohan Rao, entitledFast Transforms: Algorithms, Analyses, Applications, published by Academic Press, 1983, which is hereby incorporated by reference). However, since DHT only requires the addition and subtraction operations, a much larger block size is possible and the complexity is drastically reduced from DCT.

One embodiment of the invention uses a variable block size for the transform and uses different transforms for different block sizes. As an example, DCT will transform small blocks, and DHT will transform larger blocks. Variable block size transforms permit the block size for the transform to be the same as the block size in motion estimator to generate the better coefficient de-correlating effects than the small fixed block size transforms.

The quality of the encoded video is controllable by the quantizer Q105(as shown inFIG. 1B), which “quantizes” the transformed coefficients into discrete levels based on a quantization parameter (QP). The larger the QP, the small the number of levels that are required, and hence a smaller number of bits are required to represent the coefficients. QP is carefully adjusted to balance the rate-distortion trade-offs in reduced quality. After the quantization step, many coefficients in the transformed residue matrix become zero. To reduce the bit rate, the quantized transform coefficients in the quantized transformed residue matrix133are scanned through a selected zigzag scanning method, which scans the coefficients from the low frequency components to high frequency components, or in the reverse direction. This step converts the n dimensional coefficients into one-dimensional sequences.

The scanned one-dimensional sequence is then encoded through run-level coder106, which generates the (number of leading zero, value of non-zero coefficient) symbol sequence134information. This sequence of (run, level) symbols is then encoded through an entropy coder107(e.g., Huffman or Arithmetic coding) to further reduce the rate. The entropy coder107performs entropy encoding on the sequence of (run, level) sequence symbols134and the motion vector111, and combines them to generate the encoded block135.

The encoded block information is fed into the sequence wrapper to wrap the encoded blocks into n dimensional frame sequence. The output from the sequence wrapper123is the encoded frame sequence121, which can be sent to transmission channels or storage media. The overall flow of an n dimensional signal sequence encoding process is illustrated inFIGS. 19A and 19B, discussed below.

The n dimensional signal sequence decoding process is much simpler than the encoding process.FIG. 1Cshows he structure for the n dimensional sequence decoder. The block decoder118has already been described in the previous description ofFIG. 1B. The sequence un-wrapper181un-wraps the encoded frame sequence data into encoded blocks188. The entropy decoder182decomposes each encoded block into a motion vector187and a sequence of sequence of (run, level) symbols189. The run-level decoder183reconstructs the transformed residue matrix190from the sequence of (run level) symbols.

Motion Estimation (ME) Cost Function

The most computational intensive module in the system illustrated inFIG. 1Bis the “Motion Estimator (ME)” (or block matching searcher) module103. In the past, the ME (block matching search) is based on minimizing the error measure between the current block and the target displaced blocks in the reference frames. The error measure is typically specified in some kind of “difference energy measure” like “Sum of Square Error (SSE)” or “Sum of Absolute Error (SAE)” between the pixels of the two blocks, with SAE as the mostly widely used measure for ME search, to save computation time (as taught in chapter 6 of a book by Iain E. G. Richardson, entitledVideo Codec Design, published by John Wiley & Sons, Ltd., 2002, which is hereby incorporated by reference).

Using a “difference energy measure” like SAE or SSE does not generate the minimum bit rate. Instead, “entropy measure”, which measures the amount of information (in terms of number of bits) needed to encode the current block, given the reference frames, should be used as the primary cost function guiding the ME searches to find the best target block. This is because in most of the source signal encoding case, the target of encoder is to obtain the minimum bit rate under certain quality (or distortion) restriction.

One embodiment of the invention includes a method to guide the ME search to find the minimum entropy for the target block. Since entropy information for the current block cannot be exactly calculated until the block encoding is completed, if we select the entropy as the search cost function, the difficulty is to find an accurate estimation of the entropy during the ME search step. An adaptive cost function estimator CFE addresses this issue. One embodiment of this concept is described in the following. Let J be the estimated cost for our target search objective. J can be decomposed into two main portions:
J=Ja+Je(1A)
where Ja is the portion of the cost value (in the final target cost C), which can be accurately determined at this estimation stage; Je is the portion of the cost value which cannot be accurately determined at this stage. Furthermore, we can represent Je as the following parameterized function:

Je=Je⁡[{model⁢⁢parameters}]⁢({input⁢⁢variable})=Je[K⁢⁢1,K⁢⁢2,…⁢]⁢(X⁢⁢1,X⁢⁢2,…)(1⁢B)
where Je=C−Ja, and Je is a parameterized model function to estimate the uncertain portion for the target cost C. {X1, X2, . . . } are input variables to the cost estimation function Je, and {K1, K2, . . . } are the model parameters for the cost estimation function Je.

To achieve a good estimation, the cost function input variables {X1, X2, . . . } are extracted from certain search state information during the ME search. As shown inFIG. 1B, we have a cost function input parameter extractor CFIE128taking the relevant current search state data129to generate the values of the input variables for the cost function J. Note that after finishing encoding the current block, the estimated portion of the cost can be accurately calculated as Ce=C−Ja, since C can be accurately determined after the block encoding. With Ce, we can than apply supervised learning algorithm to train and adapt the model parameters {K1, K2, . . . } to enhance the estimation accuracy.

One embodiment of the invention applies the least mean squares (LMS) algorithm to adjust the model parameters {K1, K2, . . . } (as taught in chapter 6 of the book by Bernard Widrow and Samuel D. Stearns, entitledAdaptive Signal Processing, published by Prentice-Hall, Inc. in 1985, which is hereby incorporated by reference). LMS can be considered a single layer neural network structure, which has certain limit in its functional modeling power.

To achieve better estimation accuracy, another embodiment of the invention uses multiple layer neural network models to learn the functional relationship between the cost function input variables and the target search objective cost. For example, one model is the multi-layer neural network model taught by Tsu-Chang Lee in the paper entitledA Multi-Layer Feed-Forward Neural Network With Dynamically Adjustable Structures, in the Conference Proceedings of the 1990 IEEE International Conference on Systems, Man, and Cybernetics published by IEEE Press., PP. 367-369.

The selection of cost function input variables {X1, X2, . . . } are very critical for the accuracy and computation efficiency of the system. The following are criteria to select the cost function input variables:

Cost Function Input Parameter Selection Criteria:

C1—Simplicity criteria: The input variables have to be easily extractable from the search states during the ME search processC2—Relevancy criteria: The input variables have to be selected based on the degree of relevancy to the target objective costC3—Divide and conquer refinement criteria: Decompose a cost function J into separable contributing component functions J1, J2, . . .C4—Input variables factoring criteria: Decompose a input parameter Xi into separable contributing components Xi1, Xi2, . . . to create more input variables for better accuracy and control/trackingC5—Simple substitute criteria: If one input parameter Xi is difficult to obtain or too expensive to calculate, it can be substituted by a simpler variable Xi′.

One embodiment of the innovative framework outlined above, uses the ME cost function specified as:
J=Rate—T(T(residue)|QP)+Rate—MV(MV)  (2)
where Rate_*( ) is the number of bits for the signal through each of its own encoding method such as Rate_T, Rate_MV, etc.; T(residue) is the transform of the residue matrix114(or “Block Prediction Difference” BPD); and QP is the quantization parameter.

In one embodiment of the invention, J is evaluated by finding an approximation of Rate_T(T(residue)|QP) during the ME search process, since Rate_MV( ) can be easily determined through a table lookup, we now have Ja=Rate_MV( ) and Je=Rate_T(T(residue)|QP), which can be decomposed into frequency bands for estimation (according to cost function input parameter selection criteria C3, C4).
Rate—T(T(residue)|QP)=Σi=0˜kRate(T_Band—i(residue)|QP)+Rate(High_Freq)  (3A)
where Rate(T_Band_i(residue)|QP), i=0−k is the rate for frequency band i in the transformed residue matrix, and Rate(High_Freq) is the bits required to encode the remaining high frequency components above band k.

In most current video compression cases, DCT is used to transform the residue matrix to de-correlate the residue matrix coefficients. One embodiment uses the first few low frequency DCT energy sub-bands and the remaining total high frequency energy as input variables for the residue rate estimation function. Since it is more expensive to calculate the DCT, we can use some simpler transforms, such as the Hadamard Transform for the energy band estimation purpose (according to cost function input parameter selector criteria C5). In the very simple case, when k=0, equation (3A) becomes:

Ideally, we would want to use the energy of T_AC(residue) as input parameter to estimate Rate_T_AC( ). However, it would require performing the DCT transform on the T(residue) matrix. Since DCT is an energy preserving transform in L2 sense (meaning the energy will be preserved if the measure is defined as Sum of Square of the matrix elements—SS). Hence, if we take the SS of the AC matrix of the residue before transform, it would yield the same energy as taking the transform. To further simplify the computation, we can use Sum of Absolute Value—SA of the AC matrix as an approximation (this is again an application of cost function input parameter selection criteria C5). SA of the AC matrix is called AC_Norm(residue), and is the Norm value of the AC matrix for the residue matrix:
AC_Norm(FPD)=ΣijABS(FPDij−DC(FPD))  (4)

This provides an approximation for the entropy measure:
J=R—DC(DC(residue)/QP—dc)+R—AC(AC_NORM(residue)/QP—ac)+R—MV(MV)  (5)
where R_DC( ) and R_MV( ) can be evaluated accurately through simple table lookups; and R_AC( ) can be approximated through the parameterized cost function Je described earlier. The relationship between R_AC and AC_NORM depends on the coding methods used, and in one implementation of the invention, can be fitted by linear (201) or piece-wise linear (202) models, with AC_Norm and R_AC as the x and y axes, respectively, as illustrated inFIG. 2.

In one embodiment of the invention applied to MPEG4, the following approximation for R_AC( ) is used:
R—AC(AC_NORM|QP—ac)=AC_NORM/(2*QP—ac)  (6A)

In another embodiment of the invention, the following linear model is used:
R—AC(AC_NORM(FPD)|QP—ac)=K*AC_NORM/QP—ac(6B)

In general, K would vary based on local statistics of the FPD. One embodiment of the invention can determine the local statistical relationship between R_AC and AC_NORM, by using an adaptive method to dynamically adjust the K factor specified above.

FIG. 3(also shown as part ofFIG. 1B) shows an embodiment of the adaptive cost function estimator structure described earlier. Here the cost function is entropy based, i.e., the target objective cost for ME (block matching search) is to minimize the number of bits required to encode the current block.FIG. 3includes a motion estimator ME103, an encoding loop, an entropy coder107and an adaptive entropy based cost function estimator112. During the ME search process, the entropy (rate) is estimated through entering a set of input parameters109to the adaptive entropy based cost function estimator112. For each block, the actual encoded rate108from the entropy coder107will be compared with the estimated rate110, and the rate error113will be fed back to the adaptive entropy based cost function estimator112to adjust the cost function model parameters to reduce the rate error113. Again, following the function estimation framework described earlier, the entropy based cost function can be decomposed into two portions, as specified below:
J˜=R_Accurate+R_Estimation  (7)
R_Accurate is the portion of the rate that can be accurately determined based on certain input parameters.

In one embodiment of the invention, R_Accurate contains R_DC and R_MV, i.e.,
R_Accurate=R—DC(DC/QP—dc)+R—MV(MV)  (8A)
R_Estimation is the estimated portion of the block and in general is dependent on a set of input parameters to the lookup table.

In general, R_Estimation is dependent on certain set of characteristic parameters {Xi} related to the current block:
R_Estimation=F(X1, X2, . . . )  (8B)
After entropy coder107inFIG. 1B, the rate can be accurately calculated. With R_Actual108, we can adjust the function F( ) using some learning algorithms.

In one embodiment of the invention, R_Estimation is estimated as a linear combination of the block characteristic parameters, i.e.,
R_Estimation=ΣiKi*(Xi−Θi)  (9)
where {Xi} are the input parameters, {Θi} are the threshold of input parameters, and {Ki} are the weight factors.

One embodiment of the invention applies the LMS learning algorithm (as taught in chapter 6 of the book by Bernard Widrow and Samuel D. Stearns, entitledAdaptive Signal Processing, published by Prentice-Hall, Inc. in 1985, which is hereby incorporated by reference) to train the weight factors {Ki} through, and keep the threshold values {Θi} pre-fixed or dynamically adjustable.

One embodiment of the method specified above can accurately look up the R_DC( ), R_MV_X( ), and R_MV_Y( ) (and R_MV_F( ) for multiple frame reference cases, as used in H.264), and adaptively adjust the K factor for R_AC( ) using the linear model. In this case:
J=R—DC(DC/QP—dc)+R—MV(MV)+K*(AC_NORM−AC_THRESHOLD)/QP—ac(10)
Let R be the rate of the block after entropy coding (including the rate for DCT and MV), and let J be the estimation of the rate of the block, then K can be adjusted through the following procedure:
ΔK=μ*(R−J)*(AC_NORM−AC_THRESHOLD)/QP—ac(11)
where ΔK is the adjustment of K factor; (R−J) is the rate error; μis the learning factor, usually set in such a way to balance the convergence speed and stability; AC_THRESHOLD is the threshold value characterizing the minimum AC_NORM value where AC Rate remains non-zero. AC_THRESHOLD can be pre-fixed to certain value or dynamically adjustable.

In one embodiment of this invention, AC_THRESHOLD can be adjusted according to the following process (specified in C computer language pseudo code, as taught in Appendix D of the book by Tsu-Chang Lee, entitledStructure Level Adaptation for Artificial Neural Network, published by Kluwer Academic Publishers, Inc. in 1991, which is hereby incorporated by reference):

Zero_Point = Th0;for each block {  // block processing loop. . .If {Actual AC Rate == 0} {Zero_Point = α * AC_NORM + (1 − α ) * Zero_Point;AC_THRESHOLD = m * Zero_Point;}}
where Zero_Point is a variable tracking the zero rate AC_NORM locations; Zero_Point is initialized to the value Th0 at the beginning of the process; α is an averaging window control factor, which is a small positive number larger than zero; m is a scaling factor to control the location of the AC_THRESHOLD based on the average Zero_Point value.

It can be shown that the method to adjust K in (11) will minimize the mean square error of the rate function:
minE{(R−J)2}  (12)

In general, multiple parameters determine R_Estimation, as specified in (9), in which case the following adaptation method for K factors can be used:
ΔKi=μ*(R−J)*(Xi−Θi), for eachi(13)
If more accurate estimation is needed, more advanced learning method, like Neural Network algorithm can be used to learn the functional relation between R_Estimation and {Xi}.

One embodiment applies the multi-layer neural network learning algorithm (taught by Dr. Tsu-Chang Lee in the paper entitledA Multi-Layer Feed-Forward Neural Network With Dynamically Adjustable Structures, in the Conference Proceedings of the 1990 IEEE International Conference on Systems, Man, and Cybernetics published by IEEE Press., pp. 367-369.) to train the system.

The entropy based cost function (5) will in general create a lower bit rate and better subjective image quality compared with “Difference Energy Measure” based cost functions. It should be noted that the adaptive cost function estimation method and structure taught in the above texts, shown inFIG. 1BandFIG. 3can be applied to any cost function, which can include any kind of system objective parameters, including rate, distortion, or even some overall system performance indexes, like computing cycle, power consumption, and similar performance indexes. It should also be noted that the adaptive cost function estimation method and structure taught in the above texts, shown inFIG. 1BandFIG. 3, can be used to dynamically adjust the cost function estimator to improve the system quality and performance while the encoding system100is encoding n dimensional signal sequences. The method taught here can also be used to structure, organize, or train the estimator based on pre-collected statistic samples before actual deployment to perform the encoding task.

ME Searching Method

To search for the optimal point yielding the lowest cost function value demands the searching of a large region. The computation cost is usually prohibitively high to support real time applications. One embodiment of the invention uses a method to perform large region ME search to create close to full search results with less than 1% of computation time comparing with a full search. This method is based on the fact that inside most frames in real world n dimensional frame sequences (as in typical video samples), there are only a small number of motion vector clusters and the motion vector field is continuous varying from blocks to blocks inside each cluster. Based on this fact, the motion vectors from the neighboring blocks can provide a good starting point for a ME search.

Adaptive systems developed to learn neighborhood correlation in the physical data set and map the relationship into some processing structure was developed before. In the journal paper written by Tsu-Chang Lee, entitledAdaptive Vector Quantization Using a Self-Development Neural Network, in IEEE Journal On Selected Areas In Communications, Vol.8, No.8, published by IEEE Communication Society on October 1990, pp. 1458-1471, the author taught a method to encode general source signals efficiently using a neighborhood preserving neural network structure. It was shown in that paper, that by exploiting the data relationship in the neighborhood data set, the code book search efficiency and rate-distortion performance can be drastically enhanced.

One embodiment of this invention is to generalize similar concepts discussed in the preceding journal paper and apply them specifically to the ME search problems for the n dimensional signal sequence encoding. The basic idea is to exploit the special co-relation among neighborhood sets in the n dimensional sequence of encoded blocks.

FIG. 4shows one embodiment of a fast ME search procedure, in accordance with one embodiment of the invention. First, a set of points can be evaluated, based on the Motion Vectors of the neighboring (possibly neighbors in the space domain and the time domain) blocks (the “Neighbor Exploit Set”), and optionally the immediate neighboring points surrounding those points, as illustrated inFIG. 6), to determine the starting point of the detail search (step401inFIG. 4). This step is followed by the detail search step402, which includes a detail evaluation of a small region around the best starting point derived from the neighbor exploit set. Step403is next, which includes a global search step involving a large region hierarchical search. Step404is next, which is followed by step400for each block in the current frame.

FIG. 5shows one example of applying this method to two-dimensional video sequence case. First, the system goes through the neighbor exploit step, where the MVs from the three neighboring blocks: Left501, Up502, Up-Right503, and Origin, are used to determine the ME search starting point for the current block500. The search locations pointed by the MVs and the immediate neighboring locations together compose the Neighbor Exploit Set (as shown inFIG. 6) for the current block. The search point yielding the lowest cost value is selected as the starting point for the detailed ME search. After the Neighborhood Exploit Step, the next step is the detail search step (402inFIG. 4). The purpose of the detail search is to do a thorough evaluation of the points surrounding the detail search starting point, which was selected based on evaluating the points in the Neighbor Exploit Set.

FIG. 7shows a specific embodiment of this step. Here, a two level search is performed within a small region702surrounding the search starting point701. In level 1, the ME search is performed for every other points. After the best point703at level 1 is determined, its immediate neighboring points704in level 0 are evaluated to determine the best point. Let the best cost-function value found so far be J_Best.

After the detail search, we then move forward to the large region hierarchical search (step403in FIG.4)—the global search step. The purpose of this is to sample the points in a large search window to find motion vectors outside the detail search region in case the neighboring blocks do not provide accurate initial starting point.

FIG. 8shows an embodiment of this step in the invention to perform an n-level hierarchical search. At each level, eight points surrounding the best point from the upper level are evaluated. For search window =+/−128 case, we have:Level 6: (64, 0), (−64, 0), (0, 64), (0, −64), (64, 64), (64, −64), (−64, 64), (−64, 64)Level 5: ({0, +−32}, {0, +−32}) from the best point in Level 6Level 4: ({0, +−16}, {0, +−16}) from the best point in Level 5Level 3: ({0, +−8}, {0, +−8}) from the best point in Level 4Level 2: ({0, +−4}, {0, +−4}) from the best point in Level 3Level 1: ({0, +−2}, {0, +−2}) from the best point in Level 2Level 0: ({0, +−1}, {0, +−1}) from the best point in Level 1

To save computation time, there is no need to search all the way down to level 0. Our experimental results show that stopping at level 3 (step size 8) already generates pretty good results. This process goes on until the final level is reached. The point with the lowest cost value during the N-Level hierarchical search is then compared with the best point from the detail search, and the one yielding the lower cost value is selected as the best ME search matching point.

It should be noted that the ME search steps illustrated inFIG. 4are quite different from most of the commonly used fast searching methods. It is found that the unique combination and order of steps401,402, and403achieve close to full search results. Here the purpose of step401is to learn the MV found from the neighboring blocks to set the detail search starting point. The purpose of step402is to thoroughly evaluate the points surrounding the best starting point found in401. This step also set a cost function value threshold for the next step. Step403goes out of the small detail search region to cover the whole search space trying to reach the neighborhood of good points if the detail search cannot find a point yielding a sufficiently low cost value.

The advantage of the ME procedure is that the search results from the previously evaluated blocks can be transferred to the blocks in their neighborhoods. This “Relay” effect propagates good results and enables some kind of local “Group Cooperation” between blocks to achieve global optimization, like the system dynamics observed in “Cellular Automata” systems (as taught on page 126 in the book by Stephen Wolfram, Theory and Applications of Cellular Automata, published by World Scientific Publishing Co. in 1986, which is hereby incorporated by reference) or “Neural Network” systems (as taught in chapter 5 of the book by Tsu-Chang Lee, entitledStructure Level Adaptation for Artificial Neural Networks, published by Kluwer Academic Publishers, 1991, which is hereby incorporated by reference).

Due to the large block data overlap between adjacent search point, one efficient approach to realize the fast search process described above is to implement a parallel array structure with certain carefully designed memory organization to evaluate multiple search points concurrently. The memory structure will need to be organized in such a way to be accessed by the processing array to retrieve random block locations in the reference frames within a minimum amount of cycles.

Fractional Grid Point Search Method

To further reduce the rate, we can perform fractional grid point search after the best integer grid point is found through performing the ME search procedure described above. In MPEG4 and H.264, multiple-tap interpolation functions are used to create h-pel pixels in q-pel mode. This is very computation intensive if performed in a ME search inner loop.

In one embodiment of the invention as shown inFIG. 9, a method to perform a ME search on the fractional points uses simplified interpolation. This method is applied for video encoding applications by using a simpler complexity filter901performing interpolation (called search interpolation) to generate the sub-pixel values for the ME search903. After the best sub-pel point is found, the more complex multi-tap filter902performing interpolation (called coding interpolation) for the Motion Compensation904calculation is used. The result is experimentally very close to the result of a ME search using true multiple-tap filters. Another embodiment of the invention can enhance the results through an adaptive motion compensation filter selection.

Further bit rate improvement can be achieved by selecting the filter generating the lower rate in the MC process904. In this case, the filter selection choice information906needs to be transmitted to the decoder so that the receiver side can be synchronized with the encoder. Another embodiment can extend this idea to allow the selecting of a filter from a set of filters in the MC step.

FIG. 10shows how to use averaging filter to calculate the sub-pixel values during a ME search. The pixel values are only available at the integer pixels (1001,1001a, etc.), and the sub-pixel location can be interpolated. One embodiment of this invention uses bi-linear interpolation to calculate the sub-pixel values for ME search reference frame. In the particular example illustrated inFIG. 10, this produces:

The characteristics of the blocks of the input frames vary inside a frame and from frame to frame. One embodiment of this invention include a adaptive coding control unit to improve the quality and performance of the encoder based on feedback signals from the encoder outputs, and optionally from some block characteristic values monitored throughout the encoding process. This adaptive coding control unit can dynamically adjusts a plurality of coding parameters in the coding, according to some feedback parameter from the encoder.

As an example, the MV range distribution across blocks and frames are quite un-even. Since a larger MV means more ME search cycles, this observation can be interpreted as an un-even ME search cycle requirement across blocks and frames. In one embodiment of an adaptive encoder, methods can be implemented to allow cycle sharing among frames and blocks for better utilization of hardware processing cycles. The coding control unit may contain a motion estimator control unit to adaptively adjust the performance of the motion estimator.

FIG. 11shows one embodiment of an adaptive ME control unit. ME Processing Unit (MEPU)1101is the engine to perform the ME search. The ME Control Function1107includes a ME Control Unit (MECU)1102, which is the unit used to control MEPU1101. MECU1102takes as inputs some ME Monitoring Parameters1104(including the MV, cost value, cycle used, and so forth), Encoder Feedback Parameters1106from coding loop1108, and some adjustable parameters stored in Parameter Memory1103, to create a set of ME Control Parameters1105(including window size, cycle allocation, termination criteria, and so forth), for adaptively controlling the MEPU1101for better utilization of MEPU cycles to achieve the optimal ME search objectives.

An alternative embodiment of the ME Control Function adjusts the ME search range for the current frame based on the MV values from the past frames. One purpose is to monitor the past X, Y (and Frame for H.264 case) motion vector values and enlarge or reduce the ME search range in each dimension (X, Y, or F) on the current frame. This “adaptive ME search window” adjustment method, can effectively reduce the bit rate needed to represent the motion vectors and, can reduce the number of cycles for a ME search.

FIG. 12illustrates one example of this ME search range adjustment method. For a given current ME search range D1201, the ME search range can be adjusted according to the following criteria:1. If the number of MVs with range larger than the Enlarge Range Threshold1204(set to 3D/4 in this example) is larger than some pre-set threshold number, then enlarge the ME search range to 2D.2. If the number of MVs with range larger than the Reduce Range Threshold1205(set to 3D/8 in this example) is smaller than some pre-set threshold number, then reduce the ME search range to D/2.

One embodiment uses the method illustrated inFIG. 12to adjust the ME search range in each independent ME search dimension (X, Y, and possibly F for H.264 multi-frame cases). In addition to the adaptive ME search range method specified above, one embodiment of the invention “Early Terminates” the ME search for each block when certain condition is met to save processing cycles. The processing cycles saved from “Early Termination” will be added to the available cycle pool. MECU will base on the cycles available in the pool to allocate and schedule the cycles for the MEPU to use. The purpose is to fully utilize the available processing cycles to achieve the best ME search results.

FIG. 13shows an embodiment of the MEPU cycle scheduler1301. The MECU cycle scheduler adjusts the ME search control parameters1303for the modules in MEPU1101based on the cycles available in the cycle pool. The ME search control parameters1303include1304the number of points evaluated, the range of the neighborhood, and so forth;1306window size, step size, and so forth;1308window size, start level, terminate level, and so forth;1310the level, number of points evaluated, and so forth; and1312the range, step size, and so forth. These search control parameters are used, respectively, in1316detail search starting point selection,1318detail search,1320hierarchical search,1322sub-pixel search, and13244MV search. The MECU cycle scheduler provides1314ME cost accuracy, terms, and so forth to the1326ME search cost calculation.

Three possible “Early Termination” embodiments are:1. SKIP situation: Check the current block against the block at the same location in the reference frame. If both blocks are “similar”, then the ME processing can be skipped. In this situation, we skip the major part of the video-encoding loop (including ME, DCT, etc.) and save a lot of cycles. One embodiment of our “Similarity Criteria” for SKIP is:a. Calculate block difference [BD] between the current block and the block at the same location in the reference frame.b. Calculate DC[BD] and AC[BD]c. If DC[BD]/QP_dc<SKIP_DC_TH AND AC[BD]/QP_ac<SKIP_AC_TH, then SKIP. SKIP_DC_TH and SKIP_AC TH are some thresholds to determine the SKIP condition. SKIP_DC_TH and SKIP_AC_TH can be fixed or dynamically adjustable according to certain feedback signals from the encoder. As an example, in one embodiment of this invention, SKIP_AC TH is set to the dynamically adjusted AC_THRESHOLD value specified above.2. Good Match Termination: At any moment during the ME search for a certain block, when the cost function value is lower than certain threshold J_Early_Terminate_TH, one embodiment of the invention terminates the ME search. This happens when a very good block match is found, which results in a low cost function value. J_Early_Terminate_TH can be a pre-fixed value or dynamically adjustable based on the certain characteristic value of the encoder. For example, in one embodiment of the invention,
J_Early_Terminate_TH=f*J_Best_Mean  (17)where f is positive number less than 1, which is used to control the J_Early_Ternimate_TH; J_Best_Mean is the moving average value of J_Best through a certain moving sampling window. In one embodiment of this invention, J_Best_Mean can be calculated simply by the following formula:
J_Best_Mean=α*J_Best+(1−α)*J_Best_Mean  (18)Where α is a small number less than 1 used to control the width of the moving averaging window.3. ME Search Give up Termination: This happens if further ME search is not likely to produce better results than the best cost value J_Best found so far. When this happens, there is no need to waste cycles to search more points for that particular block. This can be evaluated by some lower bound estimation for future search points. If the lower bound for future search points is larger than the best cost found so far, we can terminate the search without sacrificing anything. This idea can be considered as applying the A* search algorithm, which is used frequently in game tree searching, to ME searching. For example, see chapter 2 of the book by Nils J. Nilsson, entitledPrinciples of Artificial Intelligence, published by Morgan Kaufmann in 1986, which is hereby incorporated by reference. The following embodiments are an application of this idea into the ME search problem:a. After finishing the 1MV search and prior to 4MV search, the 4MV search is terminated if best cost value found from 1MV is smaller than the motion vector rate, i.e.,
J_Best(1MV)<=R—MV(4MV)  (19)This is because
R—MV(4MV)<J(4MV)=R—DC(4MV)+R—AC(4MV)+R—MV(4MV)  (20)b. In MPEG4, if a ME search is performed following some spiral pattern from the ME prediction point, then R_MV(MV) would be sorted from the search starting point in increasing order of the rate for the motion vectors. Under this condition, the ME search process can be terminated if the rate for the motion vector is larger than the best cost found so far. This is illustrated inFIG. 14. The ME search starts from some prediction point1401. As illustrated inFIG. 14, the ME search follow a spiral wave expansion pattern. In MPEG4, the MV is coded as the difference from the prediction point: R_MV=R_MV_X(Delta_X)+R_MV_Y(Delta_Y). R_MV_X( ) and R_MV_Y( ) follow the same VLC table R_MV(Delta) and both are monotonically increasing with Delta. InFIG. 14, if the R_MV(D)>=J_Best (the best cost value found so far), when ME search wave expansion reaches the Wave Front1402, the search can be terminated, since all the future R_MV( ) will be larger than R_MV(D).

An alternative embodiment of the invention applies the A* search method to prune off the number of points to be evaluated in the search process. In general, if the cost function J(p) at any given point p is larger than certain bound B(p) (which is the bounding function for J at point P), and if B(p)>J_Best, the best cost function value found so far, then the point p can be skipped without sacrificing any quality. If we perform our search process to visit points in ascending of B(p), then at any given moment, if B(p)>J_Best is satisfied, the calculation for point p can be stopped. A similar concept was discussed in U.S. Pat. No. 5,363,313 (entitledMultiple-Layer Contour Searching Method and Apparatus for Circuit Building Block Placement, invented by Tsu-Chang Lee), which shows a practical application of using the bounding function to prune off search points for block placement in IC layout. In MPEG4 coding standard, MV bit rate is monotonically increasing with the X and Y length of the motion vector, hence if we use Rate(MV) as the bounding function for the search point and order our search sequence according some “spiral” pattern, then we can use Rate(MV) as early termination cost bound.

Neighborhood Preserving Scanning Method

It was taught by Tsu-Chang Lee in Ch.5An Adaptive Neural Network Source Coderof the bookStructure Level Adaptation for Artificial Neural Neural Networks, published by Kluwer Academic Publishers, 1991, that preserving the natural neighborhood relationship in adaptive artificial signal processing structures can drastically enhance the system quality, performance, and system learning behavior Applying a similar idea to the n dimensional signal sequence encoding problem, one embodiment of this invention is to scan and process the blocks according to certain sequence to preserve the spatial and temporal distance order relationship.

One embodiment of this invention is applying similar concepts discussed in the book mentioned above to our n dimensional signal encoding problem.FIG. 16shows one embodying this “Neighbor Preserving” scanning method in two-dimensional video sequence processing. Here the blocks1603are scanned and processed according to the order as shown1604. This method is specified as the following:(a) Scan a row of group of n blocks, where n is a positive integer. Start the next row of group of the blocks from the location where the previous row end.

(b) After finishing scanning one frame, start the next frame scanning from the location where the previous frame ends and scan the rows in the reverse order as the previous frame. Repeat (a) and (b) for each frame.

This embodiment of a scanning process can preserve the neighbor relationship in the scanned sequence, i.e., the neighboring blocks in the one-dimensional sequence after scan are also spatial or temporal neighbors in the original N-dimensional sequence. In general, the neighborhood-preserving scan maximizes the matching of the following conditions:(a) The neighboring blocks in the one-dimensional sequence after scan are also neighbors in the original N-dimensional sequence.(b) Two blocks are considered neighbors, if they are adjacent blocks in the same frame (spatial neighbor), and at the same location in adjacent frames (temporal neighbor).

In one embodiment of the invention, the scanning method can be used hierarchically. In this embodiment, each N-dimensional block can also be scanned by the same method within itself. In general, it is possible to use a multi-level hierarchy for this scanning method.

One proposed neighborhood preserving scan method can also be applied to the peripheral image scanning devices, such as one or more CCD sensors and/or one or more CMOS sensors to prepare the input signals to match the signal processing order. This way, it is possible to remove the frame buffer from the input sensing devices.

FIG. 18shows an embodiment of a scanning method including CMOS or CCD image scanning. Here the light signals sensed at each pixel1801are scanned out in the neighborhood preserving order1802.

FIG. 19Aillustrates the encoding flowchart for n dimensional encoding, in accordance with one embodiment of the invention. The method begins in step1901, which includes encoding of the n dimensional signal sequence header. Step1902is next, including the adjustment of the frame control parameters for each frame. Step1903is next and includes the encoding of the frame header information. Step1904is next, which includes selecting the next block in the frame according to a “scanning pattern” to optimize certain performance criteria. Step1905is next, which includes a motion estimator. Step1906is next and includes motion compensation. Step1907is next, which includes calculating the n dimensional residue matrix. Step1908is next and includes performing an n dimensional coefficient de-correlating transform on the residue matrix. Step1909is next, which includes scanning the transformed residue matrix to generate run-level symbols. Step1910is next, which includes VLC entropy coding the run-level symbols and the motion vector. Step1911is next, which includes assembling a coded block according to a pre-defined format. Step1912is next, which includes block context learning based on the methods taught earlier in this invention. At the end of the block, the steps are repeated starting from step1904for the next block. The next step after all the next blocks in a frame are processed is step1913, which includes frame wrapping and updating the frame context states. At the end of the frame, the steps are repeated starting at step1902for the next frame.

FIG. 19Billustrates the motion search flowchart, in accordance with one embodiment of the invention. The method begins at step19030. The next step is19014, which includes selecting the next location in the search space. The next step is step19015and includes calculating the search objective function. The next step is step19016, which includes updating the best cost and state. Step19017is next and includes checking for a SKIP or early termination condition. If there is no SKIP or early termination condition, then the method is repeated, starting at step19014. If there is a SKIP or early termination condition, then the motion search ends in step19031.

Step19015inFIG. 19Bcan be further divided into steps19018and19019. Step19018includes computing the accurate components of the objective function, and step19019includes computing the estimated components of the objective function. Step19019can be further divided into steps19020and19021. Step19020includes decomposing the objective function into contributing components, and step19021includes computing each contributing component of the objective function with parameters from block context learning.

In the description herein, numerous specific details are provided, such as the description of system components and methods, to provide a thorough understanding of embodiments of the invention. One skilled in relevant arts will recognize, however, that the invention can be practiced without one or more of the specific details, or with other systems, methods, components, materials, parts, and the like. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the invention.