The HAAR-the JPEG based image compression technique using singular values decomposition

Ranjan, Rajiv; Kumar, Prabhat; Kshiramani, Naik; Singh, Vikash Kumar

doi:10.1109/icefeet51821.2022.9848400

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…The images are presented in Figures 5 and 6 CHC hybrid approach, are presented in this section. Additionally, a comparison between the suggested approach and other available methods, such as (BTC [5], AMBTC [6], MBTC [7], IBTC-KQ [8], ABTC-EQ [9], DWT-CHC [16], DCT-DLUT [19] and NE-EZW [20]) is made. All experiments in this connection were conducted using the 512 × 512 and 256 × 256 input images (8-bit grayscale images, which are Lena, Barbara, Baboon, Goldhill, Peppers, Cameraman, Boat and 24-bit color images, i.e., Airplane, Peppers, Lena, Couple, House, Zelda and Mandrill).…”

Section: Experiments Resultsmentioning

confidence: 99%

“…filter). These sub-bands are used to apply the wavelet transform in computing (DWT [14] and Wavelet [HAAR] [19]). The logic behind the decomposition of the image into the four sub-bands is explained in Figure 2.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…The Operational Principle of DWT The data matrix of the image is split into four sub-bands, i.e., LL (low-pass vertical and horizontal filter), LH (low-pass vertical and high-pass horizontal filter), HL (highpass vertical and low-pass horizontal filter) and HH (high-pass vertical and horizontal filter). These sub-bands are used to apply the wavelet transform in computing (DWT [14] and Wavelet [HAAR] [19]). The logic behind the decomposition of the image into the four sub-bands is explained in Figure 2.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…For the data processing, various wavelet families are commonly used such as Haar ("haar"), Daubechies ("db"), Coiflets ("coif"), Symlets ("sym"), Biorthogonal ("bior") and Meyer ("meyer") [23]. During the present study, the Haar wavelet transform was applied due to its comparatively modest computational needs [19].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

“…Canonical Huffman coding [16] was used to compress the reconstructed image to a great extent. The authors also compared the parameters obtained in their proposed method with those provided in the block truncation [18] and the DCT-based approaches [19].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding

Ranjan,

Kumar

2023

Entropy

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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.

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