Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

MASCOT : metadata for advanced scalable video coding tools : final report

The goal of the MASCOT project was to develop new video coding schemes and tools that provide both an increased coding efficiency as well as extended scalability features compared to technology that was available at the beginning of the project. Towards that goal the following tools would be used: - metadata-based coding tools; - new spatiotemporal decompositions; - new prediction schemes. Although the initial goal was to develop one single codec architecture that was able to combine all new coding tools that were foreseen when the project was formulated, it became clear that this would limit the selection of the new tools. Therefore the consortium decided to develop two codec frameworks within the project, a standard hybrid DCT-based codec and a 3D wavelet-based codec, which together are able to accommodate all tools developed during the course of the project

Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling

Video Coding for Machines (VCM) aims to compress visual signals for machine analysis. However, existing methods only consider a few machines, neglecting the majority. Moreover, the machine perceptual characteristics are not effectively leveraged, leading to suboptimal compression efficiency. In this paper, we introduce Satisfied Machine Ratio (SMR) to address these issues. SMR statistically measures the quality of compressed images and videos for machines by aggregating satisfaction scores from them. Each score is calculated based on the difference in machine perceptions between original and compressed images. Targeting image classification and object detection tasks, we build two representative machine libraries for SMR annotation and construct a large-scale SMR dataset to facilitate SMR studies. We then propose an SMR prediction model based on the correlation between deep features differences and SMR. Furthermore, we introduce an auxiliary task to increase the prediction accuracy by predicting the SMR difference between two images in different quality levels. Extensive experiments demonstrate that using the SMR models significantly improves compression performance for VCM, and the SMR models generalize well to unseen machines, traditional and neural codecs, and datasets. In summary, SMR enables perceptual coding for machines and advances VCM from specificity to generality. Code is available at \url{https://github.com/ywwynm/SMR}

Color image quality measures and retrieval

The focus of this dissertation is mainly on color image, especially on the images with lossy compression. Issues related to color quantization, color correction, color image retrieval and color image quality evaluation are addressed. A no-reference color image quality index is proposed. A novel color correction method applied to low bit-rate JPEG image is developed. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a 24-bit JPEG image to be shown in the 8-bit color monitor with 256-color display. The reduction in download and decode time mainly comes from the smart encoder incorporating with the proposed color reduction method after color space conversion stage. To summarize, the methods that have been developed can be divided into two categories: one is visual representation, and the other is image quality measure. Three algorithms are designed for visual representation: (1) An image-based visual representation for color correction on low bit-rate JPEG images. Previous studies on color correction are mainly on color image calibration among devices. Little attention was paid to the compressed image whose color distortion is evident in low bit-rate JPEG images. In this dissertation, a lookup table algorithm is designed based on the loss of PSNR in different compression ratio. (2) A feature-based representation for content-based image retrieval. It is a concatenated vector of color, shape, and texture features from region of interest (ROI). (3) An image-specific 256 colors (8 bits) reproduction for color reduction from 16 millions colors (24 bits). By inserting the proposed color reduction method into a JPEG encoder, the image size could be further reduced and the transmission time is also reduced. This smart encoder enables its decoder using less time in decoding. Three algorithms are designed for image quality measure (IQM): (1) A referenced IQM based upon image representation in very low-dimension. Previous studies on IQMs are based on high-dimensional domain including spatial and frequency domains. In this dissertation, a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain. (2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured. (3) A no-reference color IQM based upon colorfulness, contrast and sharpness

Highly efficient low-level feature extraction for video representation and retrieval.

PhDWitnessing the omnipresence of digital video media, the research community has raised the question of its meaningful use and management. Stored in immense multimedia databases, digital videos need to be retrieved and structured in an intelligent way, relying on the content and the rich semantics involved. Current Content Based Video Indexing and Retrieval systems face the problem of the semantic gap between the simplicity of the available visual features and the richness of user semantics. This work focuses on the issues of efficiency and scalability in video indexing and retrieval to facilitate a video representation model capable of semantic annotation. A highly efficient algorithm for temporal analysis and key-frame extraction is developed. It is based on the prediction information extracted directly from the compressed domain features and the robust scalable analysis in the temporal domain. Furthermore, a hierarchical quantisation of the colour features in the descriptor space is presented. Derived from the extracted set of low-level features, a video representation model that enables semantic annotation and contextual genre classification is designed. Results demonstrate the efficiency and robustness of the temporal analysis algorithm that runs in real time maintaining the high precision and recall of the detection task. Adaptive key-frame extraction and summarisation achieve a good overview of the visual content, while the colour quantisation algorithm efficiently creates hierarchical set of descriptors. Finally, the video representation model, supported by the genre classification algorithm, achieves excellent results in an automatic annotation system by linking the video clips with a limited lexicon of related keywords