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

Improved salient object detection via boundary components affinity

Referring to the existing model that considers the image boundary as the image background, the model is still not able to produce an optimum detection. This paper is introducing the combination features at the boundary known as boundary components affinity that is capable to produce an optimum measure on the image background. It consists of contrast, spatial location, force interaction and boundary ratio that contribute to a novel boundary connectivity measure. The integrated features are capable to produce clearer background with minimum unwanted foreground patches compared to the ground truth. The extracted boundary features are integrated as the boundary components affinity. These features were used for measuring the image background through its boundary connectivity to obtain the final salient object detection. Using the verified datasets, the performance of the proposed model was measured and compared with the 4 state-of-art models. In addition, the model performance was tested on the close contrast images. The detection performance was compared and analysed based on the precision, recall, true positive rate, false positive rate, F Measure and Mean Absolute Error (MAE). The model had successfully reduced the MAE by maximum of 9.4%

Influence of Image Classification Accuracy on Saliency Map Estimation

Saliency map estimation in computer vision aims to estimate the locations where people gaze in images. Since people tend to look at objects in images, the parameters of the model pretrained on ImageNet for image classification are useful for the saliency map estimation. However, there is no research on the relationship between the image classification accuracy and the performance of the saliency map estimation. In this paper, it is shown that there is a strong correlation between image classification accuracy and saliency map estimation accuracy. We also investigated the effective architecture based on multi scale images and the upsampling layers to refine the saliency-map resolution. Our model achieved the state-of-the-art accuracy on the PASCAL-S, OSIE, and MIT1003 datasets. In the MIT Saliency Benchmark, our model achieved the best performance in some metrics and competitive results in the other metrics.Comment: CAAI Transactions on Intelligence Technology, accepted in 201