Faculty Publications
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Item Semantic-Preserving Image Compression(IEEE Computer Society, 2020) Patwa, N.; Ahuja, N.; Somayazulu, S.; Tickoo, O.; Varadarajan, S.; Koolagudi, S.G.Video traffic comprises a large majority of the total traffic on the internet today. Uncompressed visual data requires a very large data rate; lossy compression techniques are employed in order to keep the data-rate manageable. Increasingly, a significant amount of visual data being generated is consumed by analytics (such as classification, detection, etc.) residing in the cloud. Image and video compression can produce visual artifacts, especially at lower data-rates, which can result in a significant drop in performance on such analytic tasks. Moreover, standard image and video compression techniques aim to optimize perceptual quality for human consumption by allocating more bits to perceptually significant features of the scene. However, these features may not necessarily be the most suitable ones for semantic tasks. We present here an approach to compress visual data in order to maximize performance on a given analytic task. We train a deep auto-encoder using a multi-task loss to learn the relevant embeddings. An approximate differentiable model of the quantizer is used during training which helps boost the accuracy during inference. We apply our approach on an image classification problem and show that for a given level of compression, it achieves higher classification accuracy than that obtained by performing classification on images compressed using JPEG. Our approach also outperforms the relevant state-of-the-art approach by a significant margin. © 2020 IEEE.Item Autoencoded Image Compression for Secure and Fast Transmission(Institute of Electrical and Electronics Engineers Inc., 2024) Naveen, A.K.; Thunga, S.; Murki, A.; Kalale, M.; Anil, S.With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulnerable to threats such as man-in-the-middle attacks. This paper proposes an autoencoder architecture for image compression to not only help in dimensionality reduction but also inherently encrypt the images. The paper also introduces a composite loss function that combines reconstruction loss and residual loss for improved performance. The autoencoder architecture is designed to achieve optimal dimensionality reduction and regeneration accuracy while safeguarding the compressed data during transmission or storage. Images regenerated by the autoencoder are evaluated against three key metrics: reconstruction quality, compression ratio, and one-way delay during image transfer. The experiments reveal that the proposed architecture achieves an SSIM of 97.5% over the regenerated images and an average latency reduction of 87.5%, indicating its effectiveness as a secure and efficient solution for compressed image transfer. © 2024 IEEE.