Feature-Based Video Frame Compression Using Adaptive Fuzzy Inference System

Putra, Arief Bramanto Wicaksono; Gaffar, Achmad Fanany Onnilita; Wajiansyah, Agusma; Qasim, Ismail Hasan

doi:10.1109/sain.2018.8673386

Cited by 3 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The FIS structure created is used to assess the final characteristics of the two categories of characteristics to be evaluated (the characteristics of student learning and lecturers' characteristics as supervisors) as shown in Fig. 2 [18].…”

Section: Fuzzy-based Assessmentmentioning

confidence: 99%

Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment

et al. 2021

Self Cite

View full text Add to dashboard Cite

Section: Fuzzy-based Assessmentmentioning

confidence: 99%

Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment

et al. 2021

Self Cite

View full text Add to dashboard Cite

“…An encoder is used to compress video data at a certain target bit rate. Simultaneously, the decoder decompresses the video signal to make it similar to the original [3]. Several video compression standards have developed since the 1990s.…”

Section: Introductionmentioning

confidence: 99%

Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN)

Putra

Gaffar

Sumadi³

et al. 2022

JOIV : Int. J. Inform. Visualization

Self Cite

View full text Add to dashboard Cite

In principle, a video codec is built by implementing various algorithms and their development. The next generation of codecs involves more artificial intelligence applications and their development. DCNN (Deep Convolutional Neural Network) is a multi-layer NN concept with a deep learning approach in the field of artificial intelligence development. This study has proposed a DCNN with three hidden layers for intra-frame-based video compression. DCT and fractal methods were used to compare the performance of the proposed method. The training image (obtained from the average of all down-sampled frames) is divided into several square blocks using the square block shift operation until all parts of the image are fulfilled. All pixels in each block act as input data patterns. After the training process, the trained proposed DCNN was then used to construct the feature and sub-feature image obtained through the max function operation in the feature bank and sub-feature bank. These feature and sub-feature images were then a spatial redundancy minimizer with specific manipulation techniques and simultaneously a quantizer without converting the frame's pixels to a bit-stream. The result of this process is a compressed image. Experiments on the entire dataset resulted in AAPR (Average Approximate Performance Ratio) of 147.71%, or an average of 1.5 times better than other methods. For further studies, the performance improvement of the proposed DCNN is performed by modifying its structure so that the output is direct in the form of feature and sub-feature images. Another way is to combine it with the DCT or fractal method to improve the performance of the result.

show abstract

“…The compression of video data uses an encoder, while the signal decompression uses a decoder. Video compression aims to reduce storage or reduce the transmission bandwidth of video clip content [20]. Digital images taken using continuous samples and tone tones require very large storage.…”

mentioning

confidence: 99%

Modification of a gray-level dynamic range based on a number of binary bit representation for image compression

Putra

Supriadi

Wibawa

et al. 2020

SITech

Self Cite

View full text Add to dashboard Cite

Feature-Based Video Frame Compression Using Adaptive Fuzzy Inference System

Cited by 3 publications

References 9 publications

Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment

Performance measurement of the relationship between students' learning with lecturers' characteristics as supervisors based on fuzzy-based assessment

Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN)

Modification of a gray-level dynamic range based on a number of binary bit representation for image compression

Contact Info

Product

Resources

About