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Fig. 3. Ten subbands of the image Lenna after applying 3-level 2D DWT. in a single frame.

Figure 3 Ten subbands of the image Lenna after applying 3-level 2D DWT. in a single frame.

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Compression is achieved by transmitting codebook indices instead of transmitting the image pixels. So, if the codebook is of size 128, then it will only require 7 bit to transmit the codebook indices. Vector quantizer also requires the same codebook at the decoding end. The decoder just receives the codebook indices and reconstruct the pixels of the image.
Compression is achieved by transmitting codebook indices instead of transmitting the image pixels. So, if the codebook is of size 128, then it will only require 7 bit to transmit the codebook indices. Vector quantizer also requires the same codebook at the decoding end. The decoder just receives the codebook indices and reconstruct the pixels of the image.
Fig. 2. Schematic Explanation of Vector Quantization Fig. 1. Schematic Explanation of the SOFM algorithm
Fig. 2. Schematic Explanation of Vector Quantization Fig. 1. Schematic Explanation of the SOFM algorithm
Fig. 4. Detailed schematic diagram of the encoder
Fig. 4. Detailed schematic diagram of the encoder
Fig. 6. Different images used for training purpose of the proposed algorithm (Top Left image is Lenna, Top right is Baboon, Bottom left is Couple, Bottom right is Frog)
Fig. 6. Different images used for training purpose of the proposed algorithm (Top Left image is Lenna, Top right is Baboon, Bottom left is Couple, Bottom right is Frog)
Fig. 7. Schematic of the wavelet subbands after applying 3-level 2-D DWT to an image
Fig. 7. Schematic of the wavelet subbands after applying 3-level 2-D DWT to an image
TABLE I Table I. The different notations used in this table (like cAo, etc.) are corresponding to the different subbands of the test image after applying 3-level 2-D DWT, as shown in Fig. 7. As mentioned previously for the lower wavelet subband (i.e. the subband with maximum energy content) here in this case of Table I, is C'Ag, is given higher priority in encoding (7- bits data stream is used in this case). But in case of other subbands (like cV2, cD2, and cH») 5-bits data streams are used. Similarly in case of error subbands (subbands like, cAg, CV2, CcDz, and cH») 5 bits data streams are used. The images namely Peppers, Boat, Plane, and Woman as shown in Fig. 8 are used to test the proposed method. Examples of original and reconstructed images using the proposed method are shown in Fig. 9. Different PSNR and compression ratios (CR) as obtained from the testing phase of the proposed method for different images are listed in Table- II. The compression ratios are calculated after using Huffman coding of the VQ indices. All of the experimental images were in the size of 512x512 pixels. As shown in table II, the PSNR of image Peppers at compression ratio 38.94 is 30.70 in the original reconstruction (i.e. in the first iteration), then in case of Ist error iteration (i.e. in the second iteration) this PSNR was increased to 35.18 with a compression ratio equal to 22.66. Since Huffman coded error codebook indices are supposed to be transmitted to the decoder, there is a decrease in the compression ratio. In the second error iteration (i.e. in the third iteration including the first iteration called original reconstruction) the PSNR is increased to 38.62 with further decreased compression ratio to 18.44. Simulation results of the other test images are listed in the same way in Table-lII.
TABLE I Table I. The different notations used in this table (like cAo, etc.) are corresponding to the different subbands of the test image after applying 3-level 2-D DWT, as shown in Fig. 7. As mentioned previously for the lower wavelet subband (i.e. the subband with maximum energy content) here in this case of Table I, is C'Ag, is given higher priority in encoding (7- bits data stream is used in this case). But in case of other subbands (like cV2, cD2, and cH») 5-bits data streams are used. Similarly in case of error subbands (subbands like, cAg, CV2, CcDz, and cH») 5 bits data streams are used. The images namely Peppers, Boat, Plane, and Woman as shown in Fig. 8 are used to test the proposed method. Examples of original and reconstructed images using the proposed method are shown in Fig. 9. Different PSNR and compression ratios (CR) as obtained from the testing phase of the proposed method for different images are listed in Table- II. The compression ratios are calculated after using Huffman coding of the VQ indices. All of the experimental images were in the size of 512x512 pixels. As shown in table II, the PSNR of image Peppers at compression ratio 38.94 is 30.70 in the original reconstruction (i.e. in the first iteration), then in case of Ist error iteration (i.e. in the second iteration) this PSNR was increased to 35.18 with a compression ratio equal to 22.66. Since Huffman coded error codebook indices are supposed to be transmitted to the decoder, there is a decrease in the compression ratio. In the second error iteration (i.e. in the third iteration including the first iteration called original reconstruction) the PSNR is increased to 38.62 with further decreased compression ratio to 18.44. Simulation results of the other test images are listed in the same way in Table-lII.
SIMULATION RESULTS OF THE PROPOSED METHOD FOR DIFFERENT IMAGES TABLE II (that is the result in the different methods are not available for the same images). For example, the PSNR of image Pepper achieved by FMFSVQ method [7], is 29.70 at a compression ratio of 21.74. On the other hand our proposed method can achieve PSNR of 35.18 at a compression ratio of 22.66,which represents better image quality at higher compression ratio. As demonstrated in Table IJ, our proposed method gives superior results, in terms of PSNR and compression ratio to other wavelet transform and vector quantization based compression techniques.
SIMULATION RESULTS OF THE PROPOSED METHOD FOR DIFFERENT IMAGES TABLE II (that is the result in the different methods are not available for the same images). For example, the PSNR of image Pepper achieved by FMFSVQ method [7], is 29.70 at a compression ratio of 21.74. On the other hand our proposed method can achieve PSNR of 35.18 at a compression ratio of 22.66,which represents better image quality at higher compression ratio. As demonstrated in Table IJ, our proposed method gives superior results, in terms of PSNR and compression ratio to other wavelet transform and vector quantization based compression techniques.
Fig. 8. Different images used for test purpose of the proposed algorithm (Top Left image is Plane, Top right is Woman, Bottom left is Boat, Bottom right is Peppers)
Fig. 8. Different images used for test purpose of the proposed algorithm (Top Left image is Plane, Top right is Woman, Bottom left is Boat, Bottom right is Peppers)
COMPARISON OF THE PROPOSED METHOD on the overall image compression process. It reduce the compression ratio a little bit, but increases the PSNR of the image drastically, which ultimately assures the purpose of the method for those area of image compression which requires high quality image. The proposed method provides satis- factory image quality with high compression ratio. Future works on this field may include to implement Finite State Vector Quantization (FSVQ) with combination of Wavelet transform, which may give further improvements in image quality at high compression ratios. Fig. 9. Different original Image and reconstructed images using the proposed method (b) Original Image Boat on the left, on the right reconstructed Boat with PSNR = 38.17 and CR = 17.52
COMPARISON OF THE PROPOSED METHOD on the overall image compression process. It reduce the compression ratio a little bit, but increases the PSNR of the image drastically, which ultimately assures the purpose of the method for those area of image compression which requires high quality image. The proposed method provides satis- factory image quality with high compression ratio. Future works on this field may include to implement Finite State Vector Quantization (FSVQ) with combination of Wavelet transform, which may give further improvements in image quality at high compression ratios. Fig. 9. Different original Image and reconstructed images using the proposed method (b) Original Image Boat on the left, on the right reconstructed Boat with PSNR = 38.17 and CR = 17.52
(a) Original Image Pepper on the left, on the right reconstructed Pepper with PSNR = 35.18 and CR = 22.66
(a) Original Image Pepper on the left, on the right reconstructed Pepper with PSNR = 35.18 and CR = 22.66
Related topics:
Image compressionVector Quantization
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