Entropy bottleneck network for IR image compression

Abstract

The objective of image compression is to reduce irrelevance and redundancy of the image data to be able to store or transmit data in an efficient manner by minimizing the number of bits required to represent an image accurately. JPEG is capable of achieving an image compression ratio of 10:1 with little perceptible loss in image quality using standard metrics, and has become the most widely used standard image compression in the world since its release. Traditionally, compression techniques have relied on linear transforms to approximate 2-D signals (images), and the omission of specific constituent vectors has been mostly arbitrary. These techniques can save incredible amounts of memory while retaining image integrity. Recently techniques have been developed that use neural networks to approximate these signals. These networks offer the advantage of decorrelating image data to find a series of vectors to represent an image that is smaller than traditional techniques by estimating gradient descent, thus finding the minimum number of bits required to represent an image. Expansion to the development of these architectures is happening rapidly through informed design drawing upon other fields that have recently seen increased focus such as computer vision and image analysis applications. A novel efficient neural network is proposed in this work to compress infrared images at state of the art levels while preserving overall image quality to handle the demands spanning from the daily commute to combat environments.