Abstract
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.
Highlights
C OMMONLY used still image compression algorithms, such as JPEG [55], JPEG-2000 [15], HEIF [1] and WebP [25] produce undesired artifacts when the allocated bit rate is relatively low
In this work we propose to pre-process the image by automatically editing its content, applied before its compression using JPEG standard
Our CNN-based trained editors optimize for better perceptual quality, lower JPEG distortions and color degradation
Summary
C OMMONLY used still image compression algorithms, such as JPEG [55], JPEG-2000 [15], HEIF [1] and WebP [25] produce undesired artifacts when the allocated bit rate is relatively low. Blockiness, ringing, and other forms of distortion are often seen in compressed-decompressed images, even at intermediate bit-rates. The output images from such a compression procedure are of poor quality, which may hinder their use in some applications, or more commonly, introduce annoying visual flaws
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