Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms
This addresses the problem of efficient compression on mobile platforms with low power and computation resources, though it appears incremental as it builds on existing methods like Plug-and-Play algorithms.
The authors tackled image and video compression for resource-limited platforms by proposing an ultra low-cost encoder with O(1) complexity and two neural network-based decoders, achieving superior performance and robustness against bit quantization in high-definition tests.
We consider the image and video compression on resource limited platforms. An ultra low-cost image encoder, named Block Modulating Video Compression (BMVC) with an encoding complexity ${\cal O}(1)$ is proposed to be implemented on mobile platforms with low consumption of power and computation resources. We also develop two types of BMVC decoders, implemented by deep neural networks. The first BMVC decoder is based on the Plug-and-Play (PnP) algorithm, which is flexible to different compression ratios. And the second decoder is a memory efficient end-to-end convolutional neural network, which aims for real-time decoding. Extensive results on the high definition images and videos demonstrate the superior performance of the proposed codec and the robustness against bit quantization.