Adaptive Vector Qualification Coding on Wavelet Information for data Compression
Adaptive vector quantization coding
For a given bit rate of compressed image, the subband image with the highest possible amount of the total a.c. energy in the fourthe class will be encoded by the given bit rate and the empoloyed bit rate is calculated. If the remainder bit rate is great enough, the next subband image with highest possible amount of the remaining a.c. energy in the fourth class be encoded by the book with 2 bpp, and so on. When the remainder is still great enough even all subband image of the fourth class are alredy encoded, then the subband image with the higher a.c. energy in the third class will be push up into the fourth class for encoding by the encoding by the codebook of bit rate 2 bpp. However, for the low bit rate compression, all subband image in the fourth class can not encoded with the bit rate 2 bpp. Therefore the subband images with lower a.c. energy will be pushed down into the third class for encoding with the bit rate 0.5 bpp. This may be caused zero bit encoding for the subband images in the second and the first class.
The encoding process is iteratively treated in the third class by using the codebook with 0.5 bpp.
By the mentioned procedures the codebook with different bit rate will be iteratively adapter in order to prevent the obtained bit rate for not greater than the given bit rate.
Experimental result
The two states of wavelet transform is applied to an image, then 16 subband images will be obtained as shown in Fig.1.
Fig. 1 Two states of wavelet transform
The subband image with the lowest frequency (LLLL) is contained the main energy of original. Therefore, this subband image will be encoded with 8 bpp, while the recently method for encoding the wavelet transformed data using vector quantization in [4] and [5] are shown in the Fig. 2. These two methods give a fixed pattern of number of bit per pixel for each subband image.
| | Method [4] | Method [5] | Proposed method |
| JERS-1 | 60.1278 | 55.3232 | 43.4675 |
| Land sat | 47.6863 | 44.2886 | 35.9365 |
| Lena | 30.2816 | 27.1235 | 16.84519 |
Table 1 Mean square error
The testing images with the size of 512 X 512 pixels as shown in Fig. 3(a), 4(a) and 5(a) is used for the experiments. The proposed method is applied to the image in order to obtain the compressed image with the bit rate 1.03125 bpp. The reconstructed image is shows in Fig. 3(b), 4(b) and 5(b). The mean square error of the proposed method and the method of [4] and [5] in the table 1.
| 8 bpp |
VQ 2bpp Codebook size=2x2 N=256 | VQ 0.5 bpp Codebook size=4x4 N=256 |
| VQ 2bpp Codebook size=2x2 N=256 | VQ 0.5 bpp Codebook siez=4x4 N=256 |
| VQ 0.5 bpp Codebook size=4x4 N=256 | 0 bpp |
(a) Encoding pattern of [4]
| 8 bpp | VQ 2 bpp Codebook size=2x2 N=256 |
VQ 0.5 bpp Codebook size=4x4 N=256 | VQ 0.5 Codebook size=4x4 N=256 |
| VQ 2bpp Codebook size=2x2 N=256 | VQ 0.5 bpp Codebook size=4x4 N=256 | 0 bpp | VQ 0.5 bpp Codebook size=4x4 N=256 |
| VQ 0.5 bpp Codebook size=4x4 N=256 | 0 bpp | 0 bpp | VQ 0.5 bpp Codebook size=4x4 N=16 |
| VQ 0.5 bpp Codebook size=4x4 N=256 | VQ 0.5 bpp Codebook size=4x4 N=256 | VQ 0.25 bpp Codebook size=4x4 N=16 | VQ 0.5 bpp Codebook size=4x4 N=256 |
(b) Encoding patter of[5]
Fig .2 Fixed patterns of vector quantization encoding for the wavelet transformed data
