Date of Publication :25th July 2024

Abstract:In order to overcome quantization issues in picture compression, the "Deep Interval Vector Quantization for Efficient Image Compression" method creatively mixes convolutional neural networks (CNNs) and interval arithmetic. This method uses interval arithmetic to describe quantization intervals as ranges, minimizing mistakes and enhancing reconstruction accuracy. Traditional vector quantization methods frequently result in information loss and poor image quality. CNNs are used in both the training and compression phases of the process, and their ability to capture spatial dependencies is utilized to facilitate efficient quantization. Comparing experimental evaluations against standard approaches, benchmark datasets show decreased artifacts, better compression ratios, and preserved image quality. Interval arithmetic is included into compression to improve its amplification and translation capabilities. This concurrently advances the efficiency and quality of picture compression.

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