Given a standard image codec, we compress images that may have higher resolution and/or higher bit depth than allowed in the codec's specifications, by sandwiching the standard codec between a neural pre-processor (before the standard encoder) and a neural post-processor (after the standard decoder). Using a differentiable proxy for the the standard codec, we design the neural pre-and post-processors to transport the high resolution (super-resolution, SR) or high bit depth (high dynamic range, HDR) images as lower resolution and lower bit depth images. The neural processors accomplish this with spatially coded modulation, which acts as watermarks to preserve the important image detail during compression. Experiments show that compared to conventional methods of transmitting high resolution or high bit depth through lower resolution or lower bit depth codecs, our sandwich architecture gains ~9dB for SR images and $\sim$3dB for HDR images at the same rate over large test sets. We also observe significant gains in visual quality. 

Sandwiched Image Compression: Increasing the resolution and dynamic range of standard codecs

We propose sandwiched video compression – a video compression system that wraps neural networks around a standard video codec. The sandwich framework consists of a neural pre- and post-processor with a standard video codec between them. The networks are trained jointly to optimize a rate-distortion loss function with the goal of significantly improving over the standard codec in various compression scenarios. End-to-end training in this setting requires a differentiable proxy for the standard video codec, which incorporates temporal processing with motion compensation, inter/intra mode decisions, and in-loop filtering. We propose differentiable approximations to key video codec components and demonstrate that, in addition to providing meaningful compression improvements over the standard codec, the neural codes of the sandwich lead to significantly better rate-distortion performance in two important scenarios. When transporting high-resolution video via low-resolution HEVC, the sandwich system obtains 6.5 dB improvements over standard HEVC. More importantly, using the well-known perceptual similarity metric, LPIPS, we observe 30% improvements in rate at the same quality over HEVC. Last but not least, we show that pre- and post-processors formed by very modestly-parameterized, light-weight networks can closely approximate these results. 

Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers

We propose sandwiching standard image and video codecs between pre-and post-processing neural networks. The networks are jointly trained through a differentiable codec proxy to minimize a given rate-distortion loss. This sandwich architecture not only improves the standard codec's performance on its intended content, it can effectively adapt the codec to other types of image/video content and to other distortion measures. Essentially, the sandwich learns to transmit "neural code images" that optimize overall rate-distortion performance even when the overall problem is well outside the scope of the codec's design. Through a variety of examples, we apply the sandwich architecture to sources with different numbers of channels, higher resolution, higher dynamic range, and perceptual distortion measures. The results demonstrate substantial improvements (up to 9 dB gains or up to 30% bitrate reductions) compared to alternative adaptations. We derive VQ equivalents for the sandwich, establish optimality properties, and design differentiable codec proxies approximating current standard codecs. We further analyze model complexity, visual quality under perceptual metrics, as well as sandwich configurations that offer interesting potentials in image/video compression and streaming. 

Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers

Convolutional neural networks (CNN) are a popular machine learning architecture used to address multiple image-based tasks from understanding to coding. This paper targets improving image compression efficiency by designing and optimizing an image coding framework where a standard image codec, e.g. JPEG, is combined with deep neural network based pre- and post-processing. While the pre-processing CNN targets simplifying the image to make it more amenable to compression, notably involving its down-sampling, the post-processing CNN targets enhancing the decoded image, also involving its up-sampling. To optimize the compression performance, the processing CNNs are trained involving a third CNN, so-called CNN-FakeCodec, which targets modeling the image codec output, since the encoder-decoder pair is not differentiable, thus not allowing any training. Since the available alternative coding solutions focus on minimizing the image distortion, this paper proposes a new loss function which also considers a rate component, thus allowing to jointly minimize the rate and distortion. The performance results show that the proposed coding solutions can outperform the selected benchmarks, both classical and CNN-based.

Optimizing an Image Coding Framework with Deep Learning-based Pre- and Post-Processing

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Optimizing an Image Coding Framework with Deep Learning-based Pre- and Post-Processing