A novel and efficient 8-point DCT approximation for image compression

Brahimi, Nabila; Bouden, Toufik; Brahimi, Tahar; Boubchir, Larbi

doi:10.1007/s11042-019-08325-2

Cited by 26 publications

(12 citation statements)

References 22 publications

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“…In [11], the authors presented a hardware architecture for 8 × 8 2D DCT and inverse DCT using Taylor-series expansion of trigonometric functions. In [12], the authors presented an efficient and low complexity integer approximation of the DCT for image compression. eir new approach involved replacing the bit shift elements of a variant of the signed DCT transform by zeros, in order to eliminate the bit shift operations.…”

Section: Related Workmentioning

confidence: 99%

Improving the Quality of Left-Behind Children’s Participation in Sports through Wireless Network Monitoring

Zhao

2021

Mobile Information Systems

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Urbanization is the process that people shift from rural to urban areas, which has led to large numbers of left-behind children in China. The left-behind children stay in rural regions of China while their parents work in urban areas. The left-behind children have few opportunities to participate in sports due to the lacking of concern, and it is not of high quality even though they participate in sports. Therefore, it is necessary to improve the quality of left-behind children's sports participation through wireless network monitoring. Wireless network monitoring transmits high-definition (HD) video streaming in real time to facilitate feedback timely. This paper studies the two-dimensional (2D) integer discrete cosine transform (DCT) and analyzes the reason for image distortion, then an improved DCT coefficient quantization approach is proposed for long-distance real-time transmission of HD video streaming, and a noise processing with a zero-mean noise processing is added in optimized approach to solve the image distortion problem. The experimental results show that the proposed improved approach has a good performance in reducing the blocking artifacts, and within the image reconstruction, the proposed approach improves the subjective video quality.

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Section: Related Workmentioning

confidence: 99%

Improving the Quality of Left-Behind Children’s Participation in Sports through Wireless Network Monitoring

Zhao

2021

Mobile Information Systems

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“…Since we are interested in exploring BAS-based transforms in the context of image and video coding, it is also important to attain good compaction properties [14]. BAS transforms do have high coding gain and transform efficiency measurements [31,33,34,42], and we expect to find novel transforms sharing the same properties (cf. Section 3).…”

Section: Parametrizationmentioning

confidence: 99%

A Multiparametric Class of Low-complexity Transforms for Image and Video Coding

Canterle,

da Silveira,

Bayer

et al. 2020

Preprint

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Discrete transforms play an important role in many signal processing applications, and low-complexity alternatives for classical transforms became popular in recent years. Particularly, the discrete cosine transform (DCT) has proven to be convenient for data compression, being employed in well-known image and video coding standards such as JPEG, H.264, and the recent high efficiency video coding (HEVC). In this paper, we introduce a new class of low-complexity 8-point DCT approximations based on a series of works published by Bouguezel, Ahmed and Swamy. Also, a multiparametric fast algorithm that encompasses both known and novel transforms is derived. We select the best-performing DCT approximations after solving a multicriteria optimization problem, and submit them to a scaling method for obtaining larger size transforms. We assess these DCT approximations in both JPEG-like image compression and video coding experiments. We show that the optimal DCT approximations present compelling results in terms of coding efficiency and image quality metrics, and require only few addition or bit-shifting operations, being suitable for low-complexity and low-power systems.

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“…Discrete Cosine Transform (DCT) as stated by Messaoudi et al [4]; Brahimi et al [5] and Discrete Wavelet Transform (DWT) as asserted by Boucetta and Melkemi [6]; Parkale, Nalbalwar [7] The two predominantly used approaches for the lossless compression of the image are used to divide the image into multiple sub-regions. The process of image quantization is being performed to convert the image into the frequency domain.…”

Section: Introductionmentioning

confidence: 99%