Transform and Bitstream Domain Image Classification

• URL: http://arxiv.org/abs/2110.06740v1
• Date: Wed, 13 Oct 2021 14:18:58 GMT
• Title: Transform and Bitstream Domain Image Classification
• Authors: P.R. Hill, D.R. Bull
• Abstract summary: This paper proposes two such methods as a proof of concept. The first classifies within the JPEG image transform domain (i.e. DCT transform data); the second classifies the JPEG compressed binary bitstream directly. Top-1 accuracy of approximately 70% and 60% were achieved when classifying the Caltech C101 database.
• Score: 2.4366811507669124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classification of images within the compressed domain offers significant benefits. These benefits include reduced memory and computational requirements of a classification system. This paper proposes two such methods as a proof of concept: The first classifies within the JPEG image transform domain (i.e. DCT transform data); the second classifies the JPEG compressed binary bitstream directly. These two methods are implemented using Residual Network CNNs and an adapted Vision Transformer. Top-1 accuracy of approximately 70% and 60% were achieved using these methods respectively when classifying the Caltech C101 database. Although these results are significantly behind the state of the art for classification for this database (~95%), it illustrates the first time direct bitstream image classification has been achieved. This work confirms that direct bitstream image classification is possible and could be utilised in a first pass database screening of a raw bitstream (within a wired or wireless network) or where computational, memory and bandwidth requirements are severely restricted.

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