Lossy image compression based on prediction error and vector quantisation

Ayoobkhan, Mohamed Uvaze Ahamed; Eswaran, C.; Ramakrishnan, Kannan

doi:10.1186/s13640-017-0184-3

Cited by 40 publications

(22 citation statements)

References 25 publications

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“…The four-layered feedforward neural network is not necessary to reveal the mapping relationship between variables in advance, and the backpropagation algorithm is used to learn the mapping between input and output [30,31]. The feedforward neural network can minimize the loss function between the estimated value and the real value, and is widely used in regression prediction [32,33].…”

Section: Relative Psnr Model With Feedforward Neural Network Learningmentioning

confidence: 99%

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images

Chen

Gong

et al. 2020

Entropy

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Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%.

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Section: Relative Psnr Model With Feedforward Neural Network Learningmentioning

confidence: 99%

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images

Chen

Gong

et al. 2020

Entropy

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“…PSNR equal to infinity indicates image quality remains the same with the original image. The difference is observed between CR in our method and method in [59]. For example, average CR is 99:1 in our method and 80:1 in [59] with codebook size is 256.…”

Section: B Discussionmentioning

confidence: 62%

“…The difference is observed between CR in our method and method in [59]. For example, average CR is 99:1 in our method and 80:1 in [59] with codebook size is 256. Our method outperforms and gives a good compromise between CR and PSNR.…”

Section: B Discussionmentioning

confidence: 62%

“…Now, we compared our compression method with two recent work. Ayoobkhan el al in [59] introduce a lossy compression method. The method in [59] denoted PE-VQ method uses the concept of vector quantization and prediction error.…”

Section: B Discussionmentioning

confidence: 99%

“…Ayoobkhan el al in [59] introduce a lossy compression method. The method in [59] denoted PE-VQ method uses the concept of vector quantization and prediction error. An optimal codebook is generated for VQ using a combination of two intelligent algorithms (artificial bee colony and genetic algorithms).…”

Section: B Discussionmentioning

confidence: 99%

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A Machine Learning Algorithm for Biomedical Images Compression Using Orthogonal Transforms

Kouanou¹,

Tchiotsop²,

Tchinda³

et al. 2018

IJIGSP

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Compression methods are increasingly used for medical images for efficient transmission and reduction of storage space. In this work, we proposed a compression scheme for colored biomedical image based on vector quantization and orthogonal transforms. The vector quantization relies on machine learning algorithm (K-Means and Splitting Method). Discrete Walsh Transform (DWaT) and Discrete Chebyshev Transform (DChT) are two orthogonal transforms considered. In a first step, the image is decomposed into sub-blocks, on each sub-block we applied the orthogonal transforms. Machine learning algorithm is used to calculate the centers of clusters and generates the codebook that is used for vector quantization on the transformed image. Huffman encoding is applied to the index resulting from the vector quantization. Parameters Such as Mean Square Error (MSE), Mean Average Error (MAE), PSNR (Peak Signal to Noise Ratio), compression ratio, compression and decompression time are analyzed. We observed that the proposed method achieves excellent performance in image quality with a reduction in storage space. Using the proposed method, we obtained a compression ratio greater than 99.50 percent. For some codebook size, we obtained a MSE and MAE equal to zero. A comparison between DWaT, DChT method and existing literature method is performed. The proposed method is really appropriate for biomedical images which cannot tolerate distortions of the reconstructed image because the slightest information on the image is important for diagnosis.

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