Effects of Color Quantization on JPEG Compression

Araújo, Leonardo Carneiro de; Sansão, João Pedro H.; Mario, Campos

doi:10.1142/s0219467820500266

Cited by 1 publication

(1 citation statement)

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“…The DCT operation evaluates the amplitude of changes from one pixel to another in order to identify high and low frequencies [52]. Afterwards, the quantization attenuates the high frequencies of the image that have been detected by the DCT [53]. Indeed, quantization reduces the importance of contrast areas (high frequencies) that are not easily recognized by the human eye [54].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Impact of Image Compression on the Performance of Steel Surface Defect Classification with a CNN

Benbarrad

Eloutouate

Arioua

et al. 2021

JSAN

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Machine vision is increasingly replacing manual steel surface inspection. The automatic inspection of steel surface defects makes it possible to ensure the quality of products in the steel industry with high accuracy. However, the optimization of inspection time presents a great challenge for the integration of machine vision in high-speed production lines. In this context, compressing the collected images before transmission is essential to save bandwidth and energy, and improve the latency of vision applications. The aim of this paper was to study the impact of quality degradation resulting from image compression on the classification performance of steel surface defects with a CNN. Image compression was applied to the Northeastern University (NEU) surface-defect database with various compression ratios. Three different models were trained and tested with these images to classify surface defects using three different approaches. The obtained results showed that trained and tested models on the same compression qualities maintained approximately the same classification performance for all used compression grades. In addition, the findings clearly indicated that the classification efficiency was affected when the training and test datasets were compressed using different parameters. This impact was more obvious when there was a large difference between these compression parameters, and for models that achieved very high accuracy. Finally, it was found that compression-based data augmentation significantly increased the classification precision to perfect scores (98–100%), and thus improved the generalization of models when tested on different compression qualities. The importance of this work lies in exploiting the obtained results to successfully integrate image compression into machine vision systems, and as appropriately as possible.

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Section: Theoretical Backgroundmentioning

confidence: 99%