Low-complexity 8-point DCT approximation based on angle similarity for image and video coding

Oliveira, Rodrigo Silva de; Cintra, Renato J.; Bayer, Fábio M.; Silveira, Thiago L. T. da; Madanayake, Arjuna; Leite, André Ferreira

doi:10.1007/s11045-018-0601-5

Cited by 29 publications

(27 citation statements)

References 70 publications

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“…In this section, the performance of the proposed transforms is assessed in the context of image compression [17] as suggested in [6,12,13,19,26,32,43].…”

Section: Image Compressionmentioning

confidence: 99%

Low-complexity rounded KLT approximation for image compression

Radünz

Bayer

Cintra

2021

J Real-Time Image Proc

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The Karhunen-Loève transform (KLT) is often used for data decorrelation and dimensionality reduction. Because its computation depends on the matrix of covariances of the input signal, the use of the KLT in real-time applications is severely constrained by the difficulty in developing fast algorithms to implement it. In this context, this paper proposes a new class of low-complexity transforms that are obtained through the application of the round function to the elements of the KLT matrix. The proposed transforms are evaluated considering figures of merit that measure the coding power and distance of the proposed approximations to the exact KLT and are also explored in image compression experiments. Fast algorithms are introduced for the proposed approximate transforms. It was shown that the proposed transforms perform well in image compression and require a low implementation cost.

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“…In this section, the performance of the proposed transforms is assessed in the context of image compression [17] as suggested in [6,12,13,19,26,32,43].…”

Section: Image Compressionmentioning

confidence: 99%

Low-complexity rounded KLT approximation for image compression

Radünz

Bayer

Cintra

2021

J Real-Time Image Proc

Self Cite

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“…CNN takes the DCT produces more obvious features. In the DCT results, the low-frequency density part is concentrated in the top left of the DCT map, and the high-frequency density part is concentrated in the bottom right [16]. This frequency distribution feature is fed to CNN for deep learning.…”

Section: Line Density Expressed By Dct Spectrummentioning

confidence: 99%

Blind Spot Obstacle Detection from Monocular Camera Images with Depth Cues Extracted by CNN

Guo

Kumazawa

Kaku³

2018

Automot. Innov.

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The images from a monocular camera can be processed to detect depth information regarding obstacles in the blind spot area captured by the side-view camera of a vehicle. The depth information is given as a classification result "near" or "far" when two blocks in the image are compared with respect to their distances and the depth information can be used for the purpose of blind spot area detection. In this paper, the proposed depth information is inferred from a combination of blur cues and texture cues. The depth information is estimated by comparing the features of two image blocks selected within a single image. A preliminary experiment demonstrates that a convolutional neural network (CNN) model trained by deep learning with a set of relatively ideal images achieves good accuracy. The same CNN model is applied to distinguish near and far obstacles according to a specified threshold in the vehicle blind spot area, and the promising results are obtained. The proposed method uses a standard blind spot camera and can improve safety without other additional sensing devices. Thus, the proposed approach has the potential to be applied in vehicular applications for the detection of objects in the driver's blind spot.

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“…If compression is applied with the better transform, it will be a boon to the medical images storage and transmission. Mostly in diagnosis, medical images play a major role in the first step of treatment and detection (Oliveira et al, 2019). Image compression of mammographic images were performed in the current research study using DCT and shearlet.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on MATLAB based JPEG Image Size Reduction Using Discrete Cosine Transform and Shearlet Transform for Mammogram Images with Potential Hospital Data Storage Applications

Srinivasan¹

2021

revistageintec

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Aim: The aim of this study was to compare Discrete cosine transform and Shearlet transform for mammographic image compression and determine better transform among them. Materials and methods: Sample mammographic images were collected- DCT (30) and Shearlet (30) for compression. Compression ratio was calculated by comparing the original and compressed image size. The significance of the data were calculated using SPSS software. Result: There was a statistical significance between DCT and shearlet based compression ratio data (p=0.035) deviation independent sample t test). Conclusion: DCT based compression ratio was higher (2.41) than the shearlet transforms (0.73). Hence, proving to be a better compression transform than its counterpart (shearlet).

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Low-complexity 8-point DCT approximation based on angle similarity for image and video coding

Cited by 29 publications

References 70 publications

Low-complexity rounded KLT approximation for image compression

Low-complexity rounded KLT approximation for image compression

Blind Spot Obstacle Detection from Monocular Camera Images with Depth Cues Extracted by CNN

Comparative Study on MATLAB based JPEG Image Size Reduction Using Discrete Cosine Transform and Shearlet Transform for Mammogram Images with Potential Hospital Data Storage Applications

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