Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization
To meet the high bit rate requirements in many multimedia applications, a lossy image compression algorithm based on Walsh–Hadamard kernel-based feature extraction, discrete cosine transform (DCT), and bi-level quantization is proposed in this paper. The selection of the quantization matrix of the b...
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MDPI AG
2022-07-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/11/7/110 |
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| author | Dibyalekha Nayak Kananbala Ray Tejaswini Kar Chiman Kwan |
| author_facet | Dibyalekha Nayak Kananbala Ray Tejaswini Kar Chiman Kwan |
| author_sort | Dibyalekha Nayak |
| collection | DOAJ |
| description | To meet the high bit rate requirements in many multimedia applications, a lossy image compression algorithm based on Walsh–Hadamard kernel-based feature extraction, discrete cosine transform (DCT), and bi-level quantization is proposed in this paper. The selection of the quantization matrix of the block is made based on a weighted combination of the block feature strength (BFS) of the block extracted by projecting the selected Walsh–Hadamard basis kernels on an image block. The BFS is compared with an automatically generated threshold for applying the specific quantization matrix for compression. In this paper, higher BFS blocks are processed via DCT and high Q matrix, and blocks with lower feature strength are processed via DCT and low Q matrix. So, blocks with higher feature strength are less compressed and vice versa. The proposed algorithm is compared to different DCT and block truncation coding (BTC)-based approaches based on the quality parameters, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at constant bits per pixel (bpp). The proposed method shows significant improvements in performance over standard JPEG and recent approaches at lower bpp. It achieved an average PSNR of 35.61 dB and an average SSIM of 0.90 at a bpp of 0.5 and better perceptual quality with lower visual artifacts. |
| first_indexed | 2024-03-09T03:33:06Z |
| format | Article |
| id | doaj.art-0a0e4865d6d24af79f34d699c106a6cd |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T03:33:06Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-0a0e4865d6d24af79f34d699c106a6cd2023-12-03T14:51:58ZengMDPI AGComputers2073-431X2022-07-0111711010.3390/computers11070110Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level QuantizationDibyalekha Nayak0Kananbala Ray1Tejaswini Kar2Chiman Kwan3School of Electronics Engineering, KIIT Deemed to be University, Odisha 751024, IndiaSchool of Electronics Engineering, KIIT Deemed to be University, Odisha 751024, IndiaSchool of Electronics Engineering, KIIT Deemed to be University, Odisha 751024, IndiaSignal Processing, Inc., Rockville, MD 20850, USATo meet the high bit rate requirements in many multimedia applications, a lossy image compression algorithm based on Walsh–Hadamard kernel-based feature extraction, discrete cosine transform (DCT), and bi-level quantization is proposed in this paper. The selection of the quantization matrix of the block is made based on a weighted combination of the block feature strength (BFS) of the block extracted by projecting the selected Walsh–Hadamard basis kernels on an image block. The BFS is compared with an automatically generated threshold for applying the specific quantization matrix for compression. In this paper, higher BFS blocks are processed via DCT and high Q matrix, and blocks with lower feature strength are processed via DCT and low Q matrix. So, blocks with higher feature strength are less compressed and vice versa. The proposed algorithm is compared to different DCT and block truncation coding (BTC)-based approaches based on the quality parameters, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at constant bits per pixel (bpp). The proposed method shows significant improvements in performance over standard JPEG and recent approaches at lower bpp. It achieved an average PSNR of 35.61 dB and an average SSIM of 0.90 at a bpp of 0.5 and better perceptual quality with lower visual artifacts.https://www.mdpi.com/2073-431X/11/7/110DCTJPEGWHTMultiple Feature ExtractionK-means |
| spellingShingle | Dibyalekha Nayak Kananbala Ray Tejaswini Kar Chiman Kwan Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization Computers DCT JPEG WHT Multiple Feature Extraction K-means |
| title | Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization |
| title_full | Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization |
| title_fullStr | Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization |
| title_full_unstemmed | Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization |
| title_short | Walsh–Hadamard Kernel Feature-Based Image Compression Using DCT with Bi-Level Quantization |
| title_sort | walsh hadamard kernel feature based image compression using dct with bi level quantization |
| topic | DCT JPEG WHT Multiple Feature Extraction K-means |
| url | https://www.mdpi.com/2073-431X/11/7/110 |
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