Ranjan Rajiv, Kumar Prabhat
Department of Information Technology, BIT Sindri, Dhanbad 828123, India.
Department of Computer Science & Engineering, National Institute of Technology Patna, Patna 800005, India.
Entropy (Basel). 2023 Sep 25;25(10):1382. doi: 10.3390/e25101382.
Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.
近来，由于对更快编码和解码的需求不断增加，图像压缩变得至关重要。为实现这一目标，本研究提出使用规范哈夫曼编码（CHC）作为熵编码器，与二进制哈夫曼码相比，其解码时间更短。对于图像压缩，将离散小波变换（DWT）与带有主成分分析（PCA）的CHC相结合。通过使用PCA引入有损方法，随后进行DWT和CHC以提高压缩效率。通过使用DWT和CHC而非单独的PCA，重建图像具有更好的峰值信噪比（PSNR）。在本研究中，我们还开发了一种结合DWT、CHC和PCA优点的混合压缩模型。随着图像数据使用的增加，为有效利用存储空间，需要更好的图像压缩技术。所提出的技术在保持高视觉质量的同时实现了高达60%的压缩率。该方法在PSNR（以dB为单位）和每像素比特数（bpp）分数方面也优于现有技术。此方法在各种彩色图像上进行了测试，包括512×512×3的Peppers和256×256×3的Couple，PSNR分别提高了17 dB和22 dB，同时bpp分别降低了0.56和0.10。对于灰度图像，即512×512的Lena和256×256的Boat，所提出的方法PSNR分别提高了5 dB和8 dB，两种情况下bpp均降低了0.02。
