CN1322472C - Quad tree image compressing and decompressing method based on wavelet conversion prediction - Google Patents
Quad tree image compressing and decompressing method based on wavelet conversion prediction Download PDFInfo
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Abstract
The present invention discloses a method for the image compression and the decompression of a forecast quad tree on the basis of wavelet conversion. Firstly, the wavelet conversion of an image is carried out, and then, the block encoding of wavelet coefficients is carried out; the size of the encoding blocks is dynamically regulated with a quad tree partitioning algorithm, and the correlation of inband wavelet coefficients is fully used; simultaneously, a forecasting process is added in the process of encoding, an important coefficient of a prior bit plane is used for forecasting the adjacent field and the subnode coefficient of the wavelet coefficients on a current bit plane, and the adjacent field and the subnode coefficient of important coefficient of the prior bit plane are taken out for being separately encoded; thereby, the clipping of the blocks is realized so as to make the shapes of the blocks conform to the actual situation; finally, entropy encoding uses arithmetic encoding on the basis of context. The present invention can obtain good image quality under the condition of a high compression ratio.
Description
Technical field
The present invention relates to a kind of compression of images and decompressing method, especially relate to a kind of prediction quaternary tree method for compressing image and corresponding decompressing method thereof based on wavelet transformation.
Background technology
Wavelet transformation has obtained using widely aspect compression of images owing to have very strong decorrelation ability.Image is behind wavelet transformation, can produce multiband structure as shown in Figure 1, Fig. 1 is the band structure figure of image after three grades of wavelet decomposition, wherein HL1, HH1, LH1 are high frequency compositions, HL2, HH2, LH2 high frequency composition, HL3, HH3, LH3 are the infra-low frequency compositions, and LL is the lowest frequency composition.Also there is certain correlativity in image through the conversion coefficient between each frequency band inside and each frequency band behind the wavelet transformation, how to utilize this correlativity, is based on the key of wavelet image coding.At present the picture coding based on wavelet transformation adopts the bit-planes technology more, it be quantize one by one, the method for progressively-encode.For the wavelet coefficient of conversion, it is arranged on the bit-plane all wavelet coefficient higher bit positions of priority encoding earlier.If a wavelet coefficient is to be 1 at this bit-plane, then claim this wavelet coefficient to become significant coefficient at this bit-planes.The bit-plane plane is paramount moves to low, and actual is that wavelet coefficient is carried out thinning process gradually.Carry out the bit-plane coding and must manage three category informations of encoding, be i.e. position information, coefficient symbols information and coefficient amplitude information.Symbolic information wherein can be passed through a bit record, and the amplitude information record is finished automatically by mobile bit-planes, and it is an encoder performance key that the record of positional information often influences.People such as Munteanu in 1999 have proposed to come record position information based on the method for quaternary tree division, by encoding block constantly being carried out the quaternary tree division, only to isolating significant coefficient, so only the number of times that needs record to divide can be determined the position of significant coefficient, obtained good compression performance, the visible article IEEE of its specific implementation Trans.Inform.Technol.Biomed., 1999, Vol.3,176-185.P.SchelKens in 2002 have proposed the method for restriction quaternary tree, encoding block is carried out the quaternary tree division, but after the area of encoding block is less than a setting value, no longer division, but piece is carried out encoding based on contextual arithmetic coding, reduced the number of times of division, make coding efficiency that further raising arranged, the visible article IEEE of its specific implementation Trans.Medical Imaging, 2002.
As previously mentioned, very strong correlativity is arranged between the wavelet subband coefficients of images, in identical frequency band, if a wavelet coefficient is a significant coefficient at current bit-planes, then the wavelet coefficient in its eight neighborhood is that the probability of significant coefficient is very big on next bit-planes, eight neighborhood relationships as shown in Figure 2, C is a wavelet coefficient among the figure, shadow region among its eight neighborhood such as the figure.In different frequency bands, if the coefficient on the node in the wavelet based space tree is a significant coefficient, then coefficient is that the probability of significant coefficient is also very big on its child node, the relation of wavelet based space tree and father and son's node as shown in Figure 3, a certain zone in the image through behind the wavelet transformation in each frequency band corresponding wavelet coefficient can set with wavelet based space and represent, as the zone of the indication of the arrow among Fig. 3 .1, the wavelet based space number may 3.2 expressions.In the wavelet based space tree, claim that the coefficient in the subband of lower frequency is the father node of the coefficient in higher frequency subbands, as in Fig. 3 .2, node 1 is the father node of node 2,3,4, node 2 is the father node of node 5,6,7,8.
Summary of the invention
The objective of the invention is to propose prediction quaternary tree compression of images and decompressing method, the picture quality that can under the condition of high compression ratio, obtain based on wavelet transformation.
For achieving the above object, at first image is carried out wavelet transformation, then wavelet coefficient is carried out block encoding, adopt the quaternary tree partitioning algorithm, the dynamic size of adjusting encoding block, made full use of the correlativity between wavelet coefficient in the band, in the process of coding, added forecasting process simultaneously, using the significant coefficient of a bit-planes predicts its neighborhood and child node coefficient at current bit-plane, the neighborhood and the child node coefficient of the significant coefficient of a last bit-planes are taken out from piece, encode separately, thereby realization is to the cutting of piece, so that the more realistic situation of the shape of piece; Last entropy coding adopt based on contextual arithmetic coding.
The concrete steps of compression method of the present invention comprise:
The first step, small echo direct transform: treat image encoded and carry out the 2-d wavelet direct transform, obtain the wavelet subband coefficients of images image;
Second step, initialization: determine the highest bit-planes, current bit-planes is changed to the highest bit-planes; Determine the smallest blocks threshold value; With whole wavelet coefficient image as an encoding block, for this encoding block is set up empty possible significant coefficient chained list and significant coefficient chained list, possible significant coefficient chained list such as will be used for writing down at wavelet coefficient to be predicted in forecasting process, the significant coefficient chained list will be used for writing down significant coefficient; Above-mentioned encoding block is added in the encoding block chained list;
The 3rd step, thinning process: on current bit-planes, to each encoding block in the encoding block chained list, coefficient present bit in its significant coefficient chained list of encoding if present bit is 1, is exported 1 to entropy coding, otherwise is exported 0 to entropy coding;
The 4th step, forecasting process: on current bit-planes, to each encoding block in the encoding block chained list, check each coefficient in the piece, want coefficient if in its eight neighborhood, have significant coefficient or its father node to attach most importance to, this wavelet coefficient is removed from encoding block, add in the possibility significant coefficient chained list, after all coefficients check out in to encoding block, the present bit of each wavelet coefficient in the coding possibility significant coefficient chained list, if present bit is 1, export 1 to entropy coder, the sign bit of this coefficient of encoding, this wavelet coefficient from removing by the significant coefficient chained list, is joined the significant coefficient chained list; If this wavelet coefficient present bit is 0, output 0 is to entropy coder;
The 5th step, fission process: on current bit-planes, check each encoding block in the encoding block chained list, if the total coefficient number in this encoding block is greater than predefined smallest blocks threshold value, and significant coefficient is arranged in the encoding block, then this encoding block is carried out the quaternary tree division; Four sub-pieces that division is obtained add in the encoding block chained list, export 1 to entropy coder, otherwise export 0 to entropy coder;
The 6th step, reset procedure: on current bit-planes, if the total coefficient number in this encoding block is smaller or equal to a predefined smallest blocks thresholding, press each wavelet coefficient in the zigzag sequential encoding piece, if current wavelet coefficient is a significant coefficient, output 1 is to entropy coder, the encode sign bit of this coefficient is removed this wavelet coefficient from encoding block, join the significant coefficient chained list; If current wavelet coefficient is inessential coefficient, then export 0 to entropy coder;
The 7th step, mobile bit-planes, putting and moving the back bit-planes is that preceding bit-planes subtracts 1, if move the back bit-planes less than zero, end-of-encode; Otherwise, repeated for the 3rd step to the 7th step.
Entropy coder of the present invention adopts based on contextual arithmetic coding; In entropy coder, adopt four groups of different context coding models to be respectively: division model, the output of the fission process that is used for encoding; Refined model, the output of the thinning process that is used for encoding; The importance model is used for the importance of code coefficient; Symbolic model is used for the sign bit of code coefficient.
The concrete steps of decompressing method of the present invention comprise:
The first step, initialization: determine the highest bit-planes, current bit-planes is changed to the highest bit-planes; Determine the smallest blocks threshold value; Empty its possibility significant coefficient chained list, significant coefficient chained list are set up and put to whole wavelet coefficient image as an encoding block; This encoding block is added in the encoding block chained list;
Second step, thinning process: to each encoding block in the encoding block chained list, on current bit-planes, the decode present bit of each significant coefficient, export a bit from entropy coder, if be output as 1, the present bit of putting current desorption coefficient is 1, otherwise the present bit of putting current desorption coefficient is 0;
The 3rd step, forecasting process: to each encoding block in the encoding block chained list, on current bit-planes, check each coefficient, want coefficient if in its eight neighborhood, have significant coefficient or its father node to attach most importance to, this wavelet coefficient is removed from encoding block, add in the possibility significant coefficient chained list, after all non-significant coefficients check out in to encoding block, the present bit of each wavelet coefficient in the decoding possibility significant coefficient chained list, output one bit from entropy coder, if be output as 1, the present bit of putting current desorption coefficient is 1, the sign bit of this coefficient of decoding; If be output as 0, the present bit of putting current desorption coefficient is 0; This wavelet coefficient from removing by the significant coefficient chained list, is joined the significant coefficient chained list;
The 4th step, fission process: to each encoding block in the encoding block chained list, on current bit-planes, if the total coefficient number in this encoding block is greater than a predefined smallest blocks thresholding, bit of output from entropy coder, if be output as 1, then this encoding block is carried out the quaternary tree division, four sub-pieces that division is obtained add in the encoding block chained list;
The 5th step, reset procedure: to each encoding block in the encoding block chained list, on current bit-planes, if the total coefficient number in this encoding block is smaller or equal to a predefined smallest blocks thresholding, press each wavelet coefficient in the zigzag order decoding block, think highly of output one bit from entropy coding, if be output as 1, the present bit of putting current desorption coefficient is 1, the sign bit of this coefficient of decoding.If be output as 0, the present bit of putting current desorption coefficient is 0; This wavelet coefficient is removed from encoding block, joined the significant coefficient chained list;
The 6th step: mobile bit-planes, putting and moving the back bit-planes is that preceding bit-planes subtracts 1, if move the back bit-planes less than zero, decoding finishes; Otherwise, repeated for second step to the 6th step;
The 7th step: wavelet inverse transformation: the wavelet coefficient image that decoding is obtained carries out the 2-d wavelet inverse transformation, obtains decoded picture.
Show that by the contrast experiment more traditional quadtree approach (SQP), restriction quadtree approach (QT_L) and the hierarchical tree method (SPIHT) of the compression performance of the inventive method all has raising in various degree.(seeing Table 1)
Table 1 the inventive method compares with other method bit rate (bpp) under identical PSNR (dB)
| Test pattern | Bit-planes | PSNR(dB) | Bit rate (bpp) | |||
| SPIHT | SQP | QT_L | PQT | |||
| Lenna | 5 | 21.75 | 0.015 | 0.012 | 0.012 | 0.012 |
| 6 | 24.37 | 0.034 | 0.030 | 0.030 | 0.029 | |
| 7 | 27.26 | 0.078 | 0.072 | 0.072 | 0.069 | |
| 8 | 30.17 | 0.169 | 0.157 | 0.156 | ?0.152 | |
| 9 | 33.09 | 0.350 | 0.333 | 0.330 | 0.323 | |
| 10 | 36.17 | 0.773 | 0.734 | 0.714 | 0.709 | |
| 11 | 40.43 | 1.758 | 1.655 | 1.584 | 1.584 | |
| 12 | 45.78 | 3.027 | 2.863 | 2.774 | 2.777 | |
| 13 | 48.76 | 4.233 | 4.055 | 3.965 | 3.968 | |
| Peppers | 5 | 21.74 | 0.016 | 0.013 | 0.013 | 0.013 |
| 6 | 24.43 | 0.034 | 0.031 | 0.030 | 0.029 | |
| 7 | 27.68 | 0.077 | 0.074 | 0.074 | 0.071 | |
| 8 | 30.75 | 0.157 | 0.1?52 | 0.153 | 0.145 | |
| 9 | 33.49 | 0.317 | 0.305 | 0.305 | 0.290 | |
| 10 | 36.37 | 0.784 | 0.730 | 0.706 | 0.690 | |
| 11 | 40.56 | 1.764 | 1.638 | 1.577 | 1.560 | |
| 12 | 45.70 | 3.019 | 2.833 | 2.751 | 2.740 | |
| 13 | 48.64 | 4.237 | 4.027 | 3.944 | 3.933 | |
| Bridge | 5 | 14.21 | 0.017 | 0.012 | 0.012 | 0.012 |
| 6 | 15.51 | 0.215 | 0.159 | 0.138 | 0.137 | |
| 7 | 19.44 | 0.886 | 0.734 | 0.659 | 0.650 | |
| 8 | 24.92 | 1.774 | 1.556 | 1.464 | 1.437 | |
| 9 | 30.42 | 2.661 | 2.374 | 2.282 | 2.245 | |
| 10 | 35.90 | 3.593 | 3.264 | 3.170 | 3.138 | |
| 11 | 41.58 | 4.594 | 4.246 | 4.153 | 4.120 | |
| 12 | 46.96 | 5.623 | 5.263 | 5.150 | 5.139 | |
| 13 | 49.48 | 6.675 | 6.300 | 6.213 | 6.184 | |
Compared with prior art have following advantage:
1, the present invention utilizes correlativity between wavelet coefficient image band inside and frequency band, belongs to coding and mixing that interband is encoded in the band.In identical frequency band,,, carry out the quaternary tree division with piece is descending,, overcome the deficiency of fixed size encoding block in the hope of utilizing the correlativity of coefficient in the piece to greatest extent with moving of bit-plane with the coefficient piecemeal.
2, added forecasting process in the process of the present invention's coding, using the significant coefficient of a bit-planes predicts its neighborhood and child node coefficient at current bit-plane, the neighborhood and the child node coefficient of the significant coefficient of a last bit-planes are taken out coding separately from encoding block, thereby realize cutting to encoding block, reduce the division number of times of encoding block, improved coding efficiency.
3, the entropy coding among the present invention adopt based on contextual arithmetic coding, four kinds of context coding models have been proposed, further improved coding efficiency.
Description of drawings
Fig. 1 is the band structure synoptic diagram of image after the small echo direct transform;
Fig. 2 is eight neighborhood synoptic diagram;
Fig. 3-1, Fig. 3-2 is wavelet based space tree and father node relation synoptic diagram;
Fig. 4 is the process flow diagram of compression method of the present invention;
Fig. 5 is initialization flowchart in the compression method of the present invention;
Fig. 6 is thinning process process flow diagram in the compression method of the present invention;
Fig. 7 is forecasting process process flow diagram in the compression method of the present invention;
Fig. 8 is fission process process flow diagram in the compression method of the present invention;
Fig. 9 is reset procedure process flow diagram in the compression method of the present invention;
Figure 10 is the process flow diagram of decompressing method of the present invention;
Figure 11 is initialization flowchart in the decompression method of the present invention;
Figure 12 is thinning process process flow diagram in the decompression method of the present invention;
Figure 13 is forecasting process process flow diagram in the decompression method of the present invention;
Figure 14 is fission process process flow diagram in the decompression method of the present invention;
Figure 15 is reset procedure process flow diagram in the decompression method of the present invention;
Figure 16 coded identification position process flow diagram;
Figure 17 decoding symbols position process flow diagram;
Figure 18 zigzag scanning sequency synoptic diagram.
Embodiment
Method for compressing image comprises following seven steps: (see figure 4)
The first step, small echo direct transform: treat image encoded and carry out the 2-d wavelet direct transform, obtain the wavelet subband coefficients of images image;
Second step, initialization: determine the highest bit-planes Pmax, Pmax=log
2| Cmax|, Cmax is the wavelet coefficient of mould maximum, current bit-planes Pcur is changed to Pmax, determine smallest blocks threshold value Tarea, with whole wavelet coefficient image as an encoding block, be that this encoding block sets up empty possible significant coefficient chained list (LPC) and significant coefficient chained list (LSC), possible significant coefficient chained list (LPC) such as will be used for writing down at wavelet coefficient to be predicted in forecasting process, and significant coefficient chained list (LSC) will be used for writing down significant coefficient.Above-mentioned encoding block is added in the encoding block chained list (LQ);
The 3rd step, thinning process: on current bit-planes Pcur, to each encoding block among the encoding block chained list LQ, coefficient present bit in its significant coefficient chained list (LSC) of encoding, if present bit is 1, output 1 refined model S to entropy coding
2, otherwise export 0 refined model S to entropy coding
2Idiographic flow such as Fig. 6.
The 4th step, forecasting process: on current bit-planes Pcur, to each encoding block among the encoding block chained list LQ, check each coefficient in the piece, want coefficient if in its eight neighborhood, have significant coefficient or its father node to attach most importance to, this wavelet coefficient is removed from encoding block, add in the possibility significant coefficient chained list (LPC), after all coefficients check out in to encoding block, the present bit of each wavelet coefficient in the coding possibility significant coefficient chained list (LPC), if present bit is 1, output 1 importance model S to entropy coder
3, the sign bit of this coefficient of encoding, (sign bit coding flow process such as Figure 16) from removing by significant coefficient chained list (LPC), joins significant coefficient chained list (LSC) with this wavelet coefficient.If this wavelet coefficient present bit is 0, output 0 importance model S to entropy coder
3, idiographic flow such as Fig. 7.
The 5th step, fission process: on current bit-planes Pcur, check each encoding block among the encoding block chained list LQ, if the total coefficient number in this encoding block is greater than predefined smallest blocks threshold value Tarea, and significant coefficient is arranged in the encoding block, then this encoding block is carried out the quaternary tree division.Four sub-pieces that division is obtained add in the encoding block chained list (LQ), the output 1 division model S to entropy coder
1, otherwise export 0 division model S to entropy coder
1Idiographic flow such as Fig. 8.
The 6th step, reset procedure: on current bit-planes Pcur, if the total coefficient number in this encoding block is smaller or equal to a predefined smallest blocks thresholding Tarea, press each wavelet coefficient (zigzag scanning sequency such as Figure 18) in the zigzag sequential encoding piece, if current wavelet coefficient is a significant coefficient, output 1 importance model S to entropy coder
3, the sign bit of this coefficient of encoding (sign bit coding flow process such as Figure 16) is removed this wavelet coefficient from encoding block, join significant coefficient chained list (LSC).If current wavelet coefficient is inessential coefficient, then export 0 to entropy coder importance model S
3Idiographic flow such as Fig. 9.
The 7th step, mobile bit-planes, putting and moving the back bit-planes is that preceding bit-planes subtracts 1 (Pcur-1 before the mobile back Pcur=), if move the back bit-planes less than zero, end-of-encode; Otherwise, repeated for the 3rd step to the 7th step.
Above-mentioned arithmetic coding context m is calculated as follows:
For dividing model S1: context
Symbol " " is for rounding.
For refined model S2, importance model S3:
The number of all significant coefficients in eight neighborhoods of context m=present encoding coefficient and the father node.
For symbolic model S4:
The number of all positive significant coefficients in eight neighborhoods of context m=present encoding coefficient and the father node.
The image decompression method corresponding with above-mentioned method for compressing image comprises following seven steps: (see figure 10)
The first step, initialization: determine the highest bit-planes Pmax, current bit-planes Pcur is changed to Pmax.Determine smallest blocks threshold value Tarea.Empty its possibility significant coefficient chained list (LPC), significant coefficient chained list (LSC) are set up and put to whole wavelet coefficient image as an encoding block.This encoding block is added in the encoding block chained list (LQ).Idiographic flow such as Figure 11.
Second step, thinning process: to each encoding block among the LQ, on current bit-planes Pcur, the present bit of each significant coefficient of decoding.Refined model S from entropy coder
2Export a bit, if be output as 1, the present bit of putting current desorption coefficient is 1, otherwise the present bit of putting current desorption coefficient is 0, idiographic flow such as Figure 12.
The 3rd step, forecasting process: to each encoding block among the encoding block chained list LQ, on current bit-planes Pcur, check each coefficient, want coefficient if in its eight neighborhood, have significant coefficient or its father node to attach most importance to, this wavelet coefficient is removed from encoding block, add in the possibility significant coefficient chained list (LPC), after all non-significant coefficients check out in to encoding block, the present bit of each wavelet coefficient in the decoding possibility significant coefficient chained list (LPC) is from the importance model S of entropy coder
3Export a bit, if be output as 1, the present bit of putting current desorption coefficient is 1, the sign bit of this coefficient of decoding (sign bit decoding process such as Figure 17).If be output as 0, the present bit of putting current desorption coefficient is 0.This wavelet coefficient from removing by significant coefficient chained list (LPC), is joined significant coefficient chained list (LSC), idiographic flow such as Figure 13.
The 4th step, fission process: to each encoding block among the encoding block chained list LQ, on current bit-planes Pcur, if the total coefficient number in this encoding block is greater than a predefined smallest blocks thresholding Tarea, from the division model S of entropy coder
1Bit of middle output if be output as 1, then carries out the quaternary tree division to this encoding block, and four sub-pieces that division is obtained add in the encoding block chained lists (LQ) idiographic flow such as Figure 14.
The 5th step, reset procedure: to each encoding block among the LQ, on current bit-planes Pcur, if the total coefficient number in this encoding block is smaller or equal to a predefined smallest blocks thresholding Tarea, press each wavelet coefficient (zigzag scanning sequency such as Figure 18) in the zigzag order decoding block, from entropy coder importance model S
3Export a bit, if be output as 1, the present bit of putting current desorption coefficient is 1, the sign bit of this coefficient of decoding (sign bit decoding process such as Figure 17).If be output as 0, the present bit of putting current desorption coefficient is 0.This wavelet coefficient is removed from encoding block, joined significant coefficient chained list (LSC), idiographic flow such as Figure 15.
The 6th step: mobile bit-planes, putting and moving the back bit-planes is that preceding bit-planes subtracts 1, if move the back bit-planes less than zero, decoding finishes; Otherwise, repeated for second step to the 6th step;
The 7th step: wavelet inverse transformation: the wavelet coefficient image that decoding is obtained carries out the 2-d wavelet inverse transformation, obtains decoded picture.
Above-mentioned decompression procedure adopts identical with encoding compression process entropy coder.
Claims (3)
1, a kind of prediction quaternary tree method for compressing image based on wavelet transformation, it is characterized in that: at first image is carried out wavelet transformation, then wavelet coefficient is carried out block encoding, adopt the quaternary tree partitioning algorithm, the dynamic size of adjusting encoding block, made full use of the correlativity between wavelet coefficient in the band, in the process of coding, added forecasting process simultaneously, using the significant coefficient of a bit-planes predicts its neighborhood and child node coefficient at current bit-plane, the neighborhood and the child node coefficient of the significant coefficient of a last bit-planes are taken out from piece, encode separately, thereby realization is to the cutting of piece, so that the more realistic situation of the shape of piece; Last entropy coding adopts and is based on contextual arithmetic coding; Its concrete steps are:
The first step, small echo direct transform: treat image encoded and carry out the 2-d wavelet direct transform, obtain the wavelet subband coefficients of images image;
Second step, initialization: determine the highest bit-planes, current bit-planes is changed to the highest bit-planes; Determine the smallest blocks threshold value; With whole wavelet coefficient image as an encoding block, for this encoding block is set up empty possible significant coefficient chained list and significant coefficient chained list, possible significant coefficient chained list such as will be used for writing down at wavelet coefficient to be predicted in forecasting process, the significant coefficient chained list will be used for writing down significant coefficient; Above-mentioned encoding block is added in the encoding block chained list;
The 3rd step, thinning process: on current bit-planes, to each encoding block in the encoding block chained list, coefficient present bit in its significant coefficient chained list of encoding if present bit is 1, is exported 1 to entropy coding, otherwise is exported 0 to entropy coding;
The 4th step, forecasting process: on current bit-planes, to each encoding block in the encoding block chained list, check each coefficient in the piece, want coefficient if in its eight neighborhood, have significant coefficient or its father node to attach most importance to, this wavelet coefficient is removed from encoding block, add in the possibility significant coefficient chained list, after all coefficients check out in to encoding block, the present bit of each wavelet coefficient in the coding possibility significant coefficient chained list, if present bit is 1, export 1 to entropy coder, the sign bit of this coefficient of encoding, this wavelet coefficient from removing by the significant coefficient chained list, is joined the significant coefficient chained list; If this wavelet coefficient present bit is 0, output 0 is to entropy coder;
The 5th step, fission process: on current bit-planes, check each encoding block in the encoding block chained list, if the total coefficient number in this encoding block is greater than predefined smallest blocks threshold value, and significant coefficient is arranged in the encoding block, then this encoding block is carried out the quaternary tree division; Four sub-pieces that division is obtained add in the encoding block chained list, export 1 to entropy coder, otherwise export 0 to entropy coder;
The 6th step, reset procedure: on current bit-planes, if the total coefficient number in this encoding block is smaller or equal to a predefined smallest blocks thresholding, press each wavelet coefficient in the zigzag sequential encoding piece, if current wavelet coefficient is a significant coefficient, output 1 is to entropy coder, the encode sign bit of this coefficient is removed this wavelet coefficient from encoding block, join the significant coefficient chained list; If current wavelet coefficient is inessential coefficient, then export 0 to entropy coder;
The 7th step, mobile bit-planes, putting and moving the back bit-planes is that preceding bit-planes subtracts 1, if move the back bit-planes less than zero, end-of-encode; Otherwise, repeated for the 3rd step to the 7th step.
2, the prediction quaternary tree method for compressing image based on wavelet transformation according to claim 1 is characterized in that: described entropy coder adopts based on contextual arithmetic coding; In entropy coder, adopt four groups of different context coding models to be respectively: division model, the output of the fission process that is used for encoding; Refined model, the output of the thinning process that is used for encoding; The importance model is used for the importance of code coefficient; Symbolic model is used for the sign bit of code coefficient.
3, a kind of prediction quaternary tree image decompression compression method based on wavelet transformation is characterized in that comprising following concrete steps:
The first step, initialization: determine the highest bit-planes, current bit-planes is changed to the highest bit-planes; Determine the smallest blocks threshold value; Empty its possibility significant coefficient chained list, significant coefficient chained list are set up and put to whole wavelet coefficient image as an encoding block; This encoding block is added in the encoding block chained list;
Second step, thinning process: to each encoding block in the encoding block chained list, on current bit-planes, the decode present bit of each significant coefficient, export a bit from entropy coder, if be output as 1, the present bit of putting current desorption coefficient is 1, otherwise the present bit of putting current desorption coefficient is 0;
The 3rd step, forecasting process: to each encoding block in the encoding block chained list, on current bit-planes, check each coefficient, want coefficient if in its eight neighborhood, have significant coefficient or its father node to attach most importance to, this wavelet coefficient is removed from encoding block, add in the possibility significant coefficient chained list, after all non-significant coefficients check out in to encoding block, the present bit of each wavelet coefficient in the decoding possibility significant coefficient chained list, output one bit from entropy coder, if be output as 1, the present bit of putting current desorption coefficient is 1, the sign bit of this coefficient of decoding; If be output as 0, the present bit of putting current desorption coefficient is 0; This wavelet coefficient from removing by the significant coefficient chained list, is joined the significant coefficient chained list;
The 4th step, fission process: to each encoding block in the encoding block chained list, on current bit-planes, if the total coefficient number in this encoding block is greater than a predefined smallest blocks thresholding, bit of output from entropy coder, if be output as 1, then this encoding block is carried out the quaternary tree division, four sub-pieces that division is obtained add in the encoding block chained list;
The 5th step, reset procedure: to each encoding block in the encoding block chained list, on current bit-planes, if the total coefficient number in this encoding block is smaller or equal to a predefined smallest blocks thresholding, press each wavelet coefficient in the zigzag order decoding block, think highly of output one bit from entropy coding, if be output as 1, the present bit of putting current desorption coefficient is 1, the sign bit of this coefficient of decoding; If be output as 0, the present bit of putting current desorption coefficient is 0; This wavelet coefficient is removed from encoding block, joined the significant coefficient chained list;
The 6th step: mobile bit-planes, putting and moving the back bit-planes is that preceding bit-planes subtracts 1, if move the back bit-planes less than zero, decoding finishes; Otherwise, repeated for second step to the 6th step;
The 7th step: wavelet inverse transformation: the wavelet coefficient image that decoding is obtained carries out the 2-d wavelet inverse transformation, obtains decoded picture.
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| US20090154567A1 (en) * | 2007-12-13 | 2009-06-18 | Shaw-Min Lei | In-loop fidelity enhancement for video compression |
| CN101335892B (en) * | 2008-04-25 | 2010-06-09 | 太原科技大学 | Hybrid distributed video encoding method based on intra-frame intra-frame mode decision |
| CN106454371B (en) | 2010-04-13 | 2020-03-20 | Ge视频压缩有限责任公司 | Decoder, array reconstruction method, encoder, encoding method, and storage medium |
| CN106067984B (en) | 2010-04-13 | 2020-03-03 | Ge视频压缩有限责任公司 | Cross-plane prediction |
| TWI678916B (en) | 2010-04-13 | 2019-12-01 | 美商Ｇｅ影像壓縮有限公司 | Sample region merging |
| ES2904650T3 (en) | 2010-04-13 | 2022-04-05 | Ge Video Compression Llc | Video encoding using multitree image subdivisions |
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| 基于小波分解的快速分形图象压缩算法 袁静 冯前进 陈武凡 杨丰，中国图象图形学报，第8卷第4期 2003 * |
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