Simulated Annealing for JPEG Quantization

JPEG is one of the most widely used image formats, but in some ways remains surprisingly unoptimized, perhaps because some natural optimizations would go outside the standard that defines JPEG. We show how to improve JPEG compression in a standard-compliant, backward-compatible manner, by finding improved default quantization tables. We describe a simulated annealing technique that has allowed us to find several quantization tables that perform better than the industry standard, in terms of both compressed size and image fidelity. Specifically, we derive tables that reduce the FSIM error by over 10% while improving compression by over 20% at quality level 95 in our tests; we also provide similar results for other quality levels. While we acknowledge our approach can in some images lead to visible artifacts under large magnification, we believe use of these quantization tables, or additional tables that could be found using our methodology, would significantly reduce JPEG file sizes with improved overall image quality.Comment: Appendix not included in arXiv version due to size restrictions. For full paper go to: http://www.eecs.harvard.edu/~michaelm/SimAnneal/PAPER/simulated-annealing-jpeg.pd

Logarithmical hopping encoding: a low computational complexity algorithm for image compression

LHE (logarithmical hopping encoding) is a computationally efficient image compression algorithm that exploits the Weber–Fechner law to encode the error between colour component predictions and the actual value of such components. More concretely, for each pixel, luminance and chrominance predictions are calculated as a function of the surrounding pixels and then the error between the predictions and the actual values are logarithmically quantised. The main advantage of LHE is that although it is capable of achieving a low-bit rate encoding with high quality results in terms of peak signal-to-noise ratio (PSNR) and image quality metrics with full-reference (FSIM) and non-reference (blind/referenceless image spatial quality evaluator), its time complexity is O( n) and its memory complexity is O(1). Furthermore, an enhanced version of the algorithm is proposed, where the output codes provided by the logarithmical quantiser are used in a pre-processing stage to estimate the perceptual relevance of the image blocks. This allows the algorithm to downsample the blocks with low perceptual relevance, thus improving the compression rate. The performance of LHE is especially remarkable when the bit per pixel rate is low, showing much better quality, in terms of PSNR and FSIM, than JPEG and slightly lower quality than JPEG-2000 but being more computationally efficient

Semantic Perceptual Image Compression using Deep Convolution Networks

It has long been considered a significant problem to improve the visual quality of lossy image and video compression. Recent advances in computing power together with the availability of large training data sets has increased interest in the application of deep learning cnns to address image recognition and image processing tasks. Here, we present a powerful cnn tailored to the specific task of semantic image understanding to achieve higher visual quality in lossy compression. A modest increase in complexity is incorporated to the encoder which allows a standard, off-the-shelf jpeg decoder to be used. While jpeg encoding may be optimized for generic images, the process is ultimately unaware of the specific content of the image to be compressed. Our technique makes jpeg content-aware by designing and training a model to identify multiple semantic regions in a given image. Unlike object detection techniques, our model does not require labeling of object positions and is able to identify objects in a single pass. We present a new cnn architecture directed specifically to image compression, which generates a map that highlights semantically-salient regions so that they can be encoded at higher quality as compared to background regions. By adding a complete set of features for every class, and then taking a threshold over the sum of all feature activations, we generate a map that highlights semantically-salient regions so that they can be encoded at a better quality compared to background regions. Experiments are presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset, in which our algorithm achieves higher visual quality for the same compressed size.Comment: Accepted to Data Compression Conference, 11 pages, 5 figure

Compression artifact suppression for color images with dual-domain SE-ARResNet

JPEG compression has been a popular lossy image compression technique and is widely used in digital imaging. Restoring high-quality images from their compressed JPEG counterparts, however, is an ill-posed inverse problem but could be of great use in improving the visual quality of images. With the representational power that convolutional neural networks (CNNs) demon- strate, we show that it is possible to suppress JPEG compression artifacts and recover visually pleasing images. To recover original high-quality and high-resolution images from JPEG compressed images, we leverage prior knowledge of JPEG compression into consideration by exploiting frequency redundancies with the CNN in discrete cosine domain and constrain the quantization loss, in addition to exploiting spatial redundancies in the pixel domain. This data-driven approach tar- gets removing compression artifacts, including blocking, blurring, ringing and banding artifacts, and recovering high-frequency information for reconstruction. We design a deep CNN in each domain and fuse the outputs with an aggregation network to produce the output image. To improve the model performance, we leverage the robustness and ability to tackle vanishing gradient problems of ResNet to build a deep network, and utilize squeeze-and- excitation block, a technique typically found beneficial in classification tasks, to this regression problem to exploit global information in a larger scale. We refer to the module proposed in this work as squeeze-and-excitation artifact removal ResNet (SE-ARResNet). Prior work in this field mainly focuses on reconstructing a grayscale image or the luminance channel of the image. We demonstrate that we can reconstruct color images effectively and robustly with the dual-domain CNN approach