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

JPEG2000-Based Semantic Image Compression using CNN

Some of the computer vision applications such as understanding, recognition as well as image processing are some areas where AI techniques like convolutional neural network (CNN) have attained great success. AI techniques are not very frequently used in applications like image compression which are a part of low-level vision applications. Intensifying the visual quality of the lossy video/image compression has been a huge obstacle for a very long time. Image processing tasks and image recognition can be addressed with the application of deep learning CNNs as a result of the availability of large training datasets and the recent advances in computing power. This paper consists of a CNN-based novel compression framework comprising of Compact CNN (ComCNN) and Reconstruction CNN (RecCNN) where they are trained concurrently and ideally consolidated into a compression framework, along with MS-ROI (Multi Structure-Region of Interest) mapping which highlights the semiotically notable portions of the image. The framework attains a mean PSNR value of 32.9dB, achieving a gain of 3.52dB and attains mean SSIM value of 0.9262, achieving a gain of 0.0723dB over the other methods when compared using the 6 main test images. Experimental results in the proposed study validate that the architecture substantially surpasses image compression frameworks, that utilized deblocking or denoising post- processing techniques, classified utilizing Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measures (SSIM) with a mean PSNR, SSIM and Compression Ratio of 38.45, 0.9602 and 1.75x respectively for the 50 test images, thus obtaining state-of-art performance for Quality Factor (QF)=5