Quantization matrix for still and moving picture decoding

Abstract: An encoder and a decoder for for still and moving picture are disclosed. The encoder has a memory for storing a default quantization matrix including a plurality of quantization elements having predetermined values. Also, a generator is provided for producing a particular quantization matrix after a number of frames. The particular quantization matrix is read in a predetermined zigzag pattern, and the reading is terminated at a selected position which is in the middle of the zigzag pattern. An end code is added after the read quantization elements of a former portion of the particular quantization matrix. The quantization elements in the default quantization matrix are read in the same zigzag pattern from a position immediately after the selected position, and producing a latter portion of the default quantization matrix. The former portion of the particular quantization matrix and the latter portion of the default quantization matrix are synthesized to form a synthesized quantization matrix.

Description: Technical Field This invention is particularly useful in the coding of still and moving pictures at very high compression. It is suitable for use in video conferencing applications over standard telephone lines as well as for other applications that require high compression. Background Art In most compression algorithms some form of loss in the decoded picture is expected. A typical method for compression that produces good results is to introduce this loss by quantizing the signal in the transform domain instead of the pixel domain. Examples of such transforms are the Discrete Cosine Transform, DCT, the wavelet transforms and the subband analysis filters. In a transform based compression algorithm, the picture is converted into the transform domain and a quantization scheme is applied to the coefficients to reduce the amount of information. The transformation has the effect of concentrating the energy into a few coefficients and noise can be introduced into these coefficients without affecting the perceived visual quality of the reconstructed picture. It is well known that some form of human visual perception system with different weighting on the quantization on different coefficients can improve the perceived visual quality. In coding standards such as the ISO/IEC JTC1/SC29/WG11 IS-13818-2(MPEG2), the quantization of the DCT coefficients are weighted by the quantization matrix. A default matrix is normally used however the encoder can choose to send new values of the quantization matrix to the decoder. This is done through the signaling in the bitstream header. US-A-5535138 discloses a decoding method using quantization matrices which are generated from quantization matrix parameters. The prior art on sending Quantization Matrix based on the MPEG-2 video standard, is to send 64 fixed values of 8-bit each if the bit signaling for using a special Quantization Matrix is set to "1". The values of the matrix in the position of higher frequency band are actually not used, especially for very low bit rate coding where a large quantization step is employed, or for an input block with very plain texture or with good motion compensation. It is also found that, in the above prior art, for any of Quantization Matrix used in different applications, the first value of quantization matrix is always set to eight, no matter whether it is low bit rate coding or high bit rate coding. One problem with this method is the amount of information that need to be sent as part of the quantization matrix. In a typical case all 64 coefficients each of 8 bits are required. This represents a total of 512 bits. If three different Quantization Matrices are required for three bands of colour information, then the total bits will be three times of that amount. This represents too much overhead for low bit rate transmissions. It results in too long a set up time or latency in the transmissions should the matrix be changed in the middle of the transmission. The second problem to be solved is the spatial masking of the human visual system. Noise in flat regions are more visible than noise in textured regions. Therefore applying the same matrix to all regions is not a good solution as the matrix is globally optimized but not locally adjusted to the activity of the local regions. The third problem to be solved is the bit saving from the variable quantization matrix value for DC. The first value in Quantization matrix is decreased for higher bit rate and flat region and increased for lower bit rate and textured region. Disclosure of Invention A method according to the present invention is set out in claim 1. Brief Description of Drawings Fig. 1A shows a diagram of an example of a default quantization matrix. Fig. 1B shows a diagram of an example of a particular quantization matrix. Fig. 2A shows a truncated quantization matrix according to the present invention. Fig. 2B shows a diagram of another example of a particular quantization matrix. Fig. 3 shows a diagram of an example of synthesized quantization matrix according to the present invention. Fig. 4 is a block diagram of an encoder. Fig. 5 is a block diagram of a decoder. Fig. 6 is a block diagram showing one of the ways for encoding the truncated quantization matrix. Fig. 7 shows a diagram of an example of a scaling truncated quantization matrix, which is to scale the value for DC only. Fig. 8 is a flow chart showing the scaling procedure for DC coefficient in a truncated quantization matrix. Fig. 9 is a block diagram of a decoder for decoding the scaled truncated quantization matrix. Best Mode for Carrying Out the Invention The current embodiment is divided into two parts. The first part of the embodiment describes the truncated quantization matrix. The second part of the embodiment describes the operation of the adaptive quantization step size scaling. Even though the embodiment describes the operations as one unit, both methods can be applied independently to achieve the desired outcome. Fig. 1A shows an example of a default quantization matrix for intra Luminance (Intra-Y) frame coding, and Fig. 1B shows an example of a particular quantization matrix that quantizes the high frequency coefficients more coarsely. Fig. 2A is an example of the truncated quantization matrix proposed by the present invention. The key to this embodiment is that the number of values in the quantization matrix to be transmitted may be less than 64. This is particularly useful especially for very low bit rate coding, where only the first 2 or 3 values are required. Fig. 4 shows an encoder using the quantization matrix for the still and moving pictures. The encoder includes a DCT converter 32, a quantizer 34, and a variable length coding unit 49. A QP generator 36 for generating quantization parameters after, e.g., every macro-block is provided. The quantization parameter can be calculated using predetermined equation after every macro-block, or can be selected from a look up table. The quantization parameters as obtained are applied to the quantizer 34 and also to a decoder which will be described in detail later in connection with Fig. 5. In Fig. 4, the encoder further has a particular QM generator 38 for generating particular quantization elements aligned in a matrix format. The particular quantization elements in matrix are generated after every video object layer (VOL) consisting of a plurality of layers. Examples of the particular quantization elements in matrix QM are shown in Fig. 1B and Fig. 2B. In the case where the video data is sent with less data amount (such as when the bit rate is low, or when the image is simple), the particular quantization elements shown in Fig. 1B is used in which large amount of quantization elements, such as 200, are used in the high frequency region. The particular quantization elements can be obtained by calculation or by using a suitable look up table. A selector 37 is provided for selecting parameters used in the calculation, or a suitable quantization elements in matrix from the look up table. The selector 37 can be operated manually by the user or automatically based on the type of the image (real picture or graphic picture) or the quality of the image. The particular quantization elements in matrix QM are applied to a truncator 40. The truncator 40 reads the particular quantization elements in matrix QM in a zigzag format, as controlled by a zigzag scan 48, from a DC component to higher frequency components, as shown by dotted lines in Fig 2A. When the truncator 40 reads a preset number of particular quantization elements in matrix, a further zigzag reading from the matrix QM of block 38 is terminated. Thereafter, an end code, such as a zero, is added by an end code adder to the end of the preset number of particular quantization elements. The preset number is determined by a setting unit 39 operated manually by a user or automatically relatively to the type or quality of the picture. According to an example shown in Fig. 2A, the preset number is thirteen. Thus, there will be thirteen particular quantization elements being read out before the termination of the zigzag reading. These read out quantization elements are referred to as quantization elements in the former portion, since they are in the former portion of the zigzag reading of the particular quantization elements in matrix QM. The quantization elements in the former portion are sent to a synthesized QM generator 44, and the same quantization elements plus the end code are sent to a decoder shown in Fig. 5. A series of these quantization elements in the former portion followed by the end code is called a simplified data QMt. A default QM generator 46 is provided for storing default quantization elements aligned in matrix, such as shown in Fig. 1A. These default quantization elements are also read out in the zigzag form by the control of zigzag scan 48. A synthesized QM generator 44 is provided for generating synthesized quantization elements in a matrix form. In the synthesized QM generator 44, the particular quantization elements in the former portion as obtained from the truncator 40, and the default quantization elements in a latter portion (a portion other than the former portion) from the default QM generator 46 are synthesized. Thus, the synthesized QM generator 44 uses the particular quantization elements in the former portion and the default quantization elements in the latter portion for synthesizing the synthesized quantization elements in matrix. Fig. 3 shows an example of a synthesized quantization elements in matrix in which the former portion F is filled with the particular quantization elements and the latter portion L is filled with the default quantisation values. In the quantizer 34, the DCT coefficients COF in matrix format are quantized by using the synthesized quantization elements in matrix from the synthesized QM generator 44, and the quantization parameter QP from the QP generator 36. Then, the quantizer 34 generates quantized DCT coefficients COF' in matrix format. The coefficients COFij and COF'ij (i and j are positive integers between 1 and 8, inclusive) have the following relationship. COF ' ij ∝ COF ij QM ij * QP Here, QMij represent quantization elements in matrix as produced from synthesized QM generator 44, QP represent a quantization parameter as produced from QP generator 36. The quantized DCT coefficients COF' are then further coded in the variable length encoding unit 49, and the compressed video data VD is output from the unit 49 and applied to the decoder shown in Fig. 5. Fig. 5 shows a decoder using the quantization matrix for the still and moving pictures. The decoder includes a variable length decoding unit 50, an inverse quantizer 52, an inverse DCT converter 62, an end code detector 56, a synthesized QM generator 54, a default QM generator 58, and a zigzag scan 60. The default QM generator 58 stores a default quantization matrix, such as that shown in Fig. 1A. It is noted that the default quantization matrix stored in the default QM generator 58 is the same one as that stored in the default QM generator 46 shown in Fig. 4. The synthesized QM generator 54 and the zigzag scan 60 are substantially the same as the synthesized QM generator 44 and the zigzag scan 48, respectively, shown in Fig. 4. The video data VD transmitted from the encoder of Fig. 4 is applied to the variable length decoding unit 50. Similarly, the quantized parameter QP is applied to inverse quantizer 52, and the simplified data QMt is applied to the end code detector 56. As described above, the simplified data QMt includes particular quantization element in the former portion in the matrix. The particular quantization elements are zigzag scanned by zigzag scan 60 and are stored in the former portion of the synthesized QM generator 54. Then, when the end code is detected by the end code detector 56, the supply of the particular quantization elements from the end code detector 56 terminates, and in turn, the default quantization elements from the default QM generator 58 are zigzag scanned in the latter portion of the synthesized QM generator 54. Thus, the synthesized quantization matrix generated in the synthesized QM generator 54 in Fig. 5 is the same as the the synthesized quantization matrix generated in the synthesized QM generator 44 in Fig. 4. Since the synthesized quantization matrix can be reproduced using the simplified data QMt, it is possible to reproduce the high quality image with less data to be transmitted from encoder to decoder. Fig. 6 shows one of the ways to code and transmit the truncated quantization matrix. Here, the unit 1 is the truncated quantization matrix determined in the unit 2 by checking different coding bit rate, different coding picture size, etc.. x1, x2, x3, ... in unit 1 are those non-zero quantization matrix values used to quantize a block of 8x8 DCT coefficients in the same position as x1, x2, x3, .... Other parts of the quantization matrix with zero values in the unit 1 means that the default value of the quantization matrix will be used. In the encoder, same part of DCT coefficients of a 8x8 block will be set to zero. The unit 3 is to scan the non-zero values in the unit 1 into a group of data with larger value being concentrated on the first part of the group. Zig-zag scan is shown here as an example. The unit 4 shows the optional part to code the scanned data by subtracting neighbouring values to obtain the smaller difference values, Δx1, Δx2, ..., as shown in Fig. 6, maybe further followed by Huffman coding or other entropy coding methods. At the same time, the number of non-zero quantization matrix values is also coded and transmitted to decoder, together with those non-zero values. There are different ways to code this information. The simplest method is to code the number by using a fixed 8-bit. Another method is to code the number by using a variable length table which is designed to use less bits to handle the most frequent cases. Alternatively, instead of coding and transmitting the number of non-zero quantization matrix values, as shown in Fig. 6, after the last non-zero value, xN, or last difference value, ΔxN (N=1, 2, 3, ...) is coded, a specific symbol is inserted into the bitstream to indicate the termination of the non-zero quantization matrix coding. This specific symbol may be a value which is not used in the non-zero value coding such as zero or a negative value. Fig. 7 is the truncated quantization matrix with scaling factor S as weighting for DC only. This scaling factor is adjusted based on the activity of individual block. The activity information can be obtained by checking the number of AC coefficients left after quantization. x1, x2, x3, ..., x9 are the non-zero values in the truncated quantization matrix to be used to quantize 8x8 DCT coefficient block, and S is the weighting for scaling up/down for the first value to adjust the quantizer for DC coefficient. Fig. 8 shows the details about the scaling procedure for the first value in quantization matrix. The unit 5 quantizes each of 8x8 block by applying the truncated quantization matrix first, followed by the required quantization step at that time for that block. The unit 6 checks the number of AC coefficients left after the above quantization, passing to the unit 7 to decide whether the weighting S in Fig. 7 is scaled up or down. If more AC coefficients left after the quantization done in the unit 5, then the weighting S can be scaled up, shown in the unit 8; otherwise scaled down, shown in the unit 9. The unit 10 scales the weighting S to adjust the first value in the quantization matrix, and the unit 11 re-quantizes the DC coefficient by using the new adjusted value for block A and output all the DC & AC coefficients to decoder. The scaling up & down can be chosen some value related to the present quantization step or a fixed value. The adjustment of the other quantization matrix values for AC coefficients can be followed the similar way. A decoder of the adaptive quantization step size scaling and truncated quantization matrix is shown in Fig. 9. In the Fig. 9, the decoded bitstream is input to the decoder. The unit 12 will decode the truncated quantization matrix, and the unit 13 will decode the quantization step for each of block. The unit 14 will decode all the DC & AC coefficients for each of block. The unit 15 will check the number of AC coefficients which are not zero, and the scaling factor can be determined in the unit 16 by using the information obtained from the unit 15 and following the same criteria as in the encoder. All DC & AC coefficients for each of block can be inversely quantized in the unit 17 by the decoded scaling quantization matrix and the decoded quantization matrix. Finally all the inversely quantized coefficients are passed to an inverse DCT transform coding unit to reconstruct image picture. The following formula are used for the quantization and inverse quantization: Quantization: For Intra DC: Level = |COF| // (QM / 2) For Intra AC: Level = |COF| * 8/(QP*QM) For Inter: Level = (|COF| - (QP*QM/32)) * 8/(QP*QM) Inverse Quantization: For Intra DC: |COF'| = Level * QM / 2 For Others: |COF'| = 0, if Level = |COF'| = (2 * LEVEL + 1) * (QP*QM /16), if LEVEL ≠ 0, (QP*QM/16) is odd |COF'| = (2 * LEVEL + 1) * (QP*QM/16) - 1, if LEVEL ≠ 0, (QP*QM /16) is even Where: COF is the transform coefficient to be quantized. LEVEL is the absolute value of the quantized version of the transform coefficient. COF' is the reconstructed transform coefficient. QP is the quantization step size of the current block. QM is the value of the quantization matrix corresponding to the coefficient to be quantized. Default value of QM is 16. The quantization matrix can be adaptively changed according to coding bit rate, coding size, as well as human visual system, so that a lot of bits can be saved by truncating and scaling the quantization matrix and encoding the values of the matrix differentially. Therefore it will increase coding efficiency, especially for very low bit rate coding. The invention being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the scope of the invention, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.

Claims: A method of generating a quantization matrix that is used in an inverse quantization step, by decoding a coded truncated quantization matrix, the method comprising: receiving a bitstream including the coded truncated quantization matrix that is obtained by coding a truncated quantization matrix, wherein the truncated quantization matrix is generated by truncating a quantization matrix having a plurality of quantization elements, and the coded truncated quantization matrix has bits aligned in the order of: bits obtained by coding quantization elements included in the truncated quantization matrix and bits obtained by coding an end code; decoding the coded truncated quantization matrix until the bits corresponding to the end code are read out to recover the plurality of quantization elements and reform them into the truncated quantization matrix; and, generating a complete quantization matrix using the reformed truncated quantization matrix by obtaining additional quantization elements which are added to the quantization elements of the reformed truncated quantization matrix to complete it. The generating method according to claim 1, wherein the end code is a value of 'O'.

Inventors: SHEN SHENG MEI, TAN THIOW KENG, SHEN, SHENG MEI, TAN, THIOW KENG
Applicants: MATSUSHITA ELECTRIC IND CO LTD, MATSUSHITA ELECTRIC INDUSTRIAL CO., LTD.
Application year: 1998
Main classification: H04N7
IPC codes: H04N7, G06T9