Optimized Pre-Compensating Compression

JPEG Image Enhancement with Pre-Processing of Color Reduction and Smoothing

JPEG is the international standard for still image encoding and is the most widely used compression algorithm because of its simple encoding process and low computational complexity. Recently, many methods have been developed to improve the quality of JPEG images by using deep learning. However, these methods require the use of high-performance devices since they need to perform neural network computation for decoding images. In this paper, we propose a method to generate high-quality images using deep learning without changing the decoding algorithm. The key idea is to reduce and smooth colors and gradient regions in the original images before JPEG compression. The reduction and smoothing can suppress red block noise and pseudo-contour in the compressed images. Furthermore, high-performance devices are unnecessary for decoding. The proposed method consists of two components: a color transformation network using deep learning and a pseudo-contour suppression model using signal processing. The experimental results showed that the proposed method outperforms standard JPEG in quality measurements correlated with human perception.

Better Compression With Deep Pre-Editing

Could we compress images via standard codecs while avoiding visible artifacts? The answer is obvious - this is doable as long as the bit budget is generous enough. What if the allocated bit-rate for compression is insufficient? Then unfortunately, artifacts are a fact of life. Many attempts were made over the years to fight this phenomenon, with various degrees of success. In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits. We design this editing operation as a learned convolutional neural network, and formulate an optimization problem for its training. Our loss takes into account a proximity between the original image and the edited one, a bit-budget penalty over the proposed image, and a no-reference image quality measure for forcing the outcome to be visually pleasing. The proposed approach is demonstrated on the popular JPEG compression, showing savings in bits and/or improvements in visual quality, obtained with intricate editing effects.

Optimal Pre-Filtering for Improving Facebook Shared Images

Online Social Networks (OSNs) have attracted a huge number of users, who store and share various images on a daily basis. As a well-known fact, most OSN platforms apply a series of lossy operations on the uploaded images, which could severely degrade the quality of the shared images, negatively affecting the user experiences. In this work, we consider the problem of significantly improving OSN-shared images through applying an optimal pre-filtering prior to image sharing, without any cooperation from the OSN platform itself. Facebook, as one of the most popular and representative OSNs, is chosen as the platform to present our designed pre-filtering strategy. We first treat Facebook as a black box, and thoroughly recover its mechanism of processing color images. Based on the precise knowledge on the image processing pipeline on Facebook, we design the pre-filter under an optimization framework, minimizing the end-to-end distortion between the shared image and the original one. Compared with the directly shared images, our proposed pre-filtering-then-sharing strategy brings significant improvements in terms of both quantitative and qualitative metrics. Extensive experimental results are provided to show the superiority of our proposed method. Finally, we discuss the strategy on how to extend our proposed technique to other OSN platforms.