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As an emerging research practice leveraging recent advanced AI techniques, e.g. deep models based prediction and generation, Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. With the rapid advances of deep feature representation and visual data compression in mind, in this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established including feature-assisted coding, scalable coding, intermediate feature compression/optimization, and machine vision targeted codec, from broader perspectives of vision tasks, analytics resources, etc. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks.

Removing the undesired moiré patterns from images capturing the contents displayed on screens is of increasing research interest, as the need for recording and sharing the instant information conveyed by the screens is growing. Previous demoiréing methods provide limited investigations into the formation process of moiré patterns to exploit moiré-specific priors for guiding the learning of demoiréing models. In this paper, we investigate the moiré pattern formation process from the perspective of signal aliasing, and correspondingly propose a coarse-to-fine disentangling demoiréing framework. In this framework, we first disentangle the moiré pattern layer and the clean image with alleviated ill-posedness based on the derivation of our moiré image formation model. Then we refine the demoiréing results exploiting both the frequency domain features and edge attention, considering moiré patterns’ property on spectrum distribution and edge intensity revealed in our aliasing based analysis. Experiments on several datasets show that the proposed method performs favorably against state-of-the-art methods. Besides, the proposed method is validated to adapt well to different data sources and scales, especially on the high-resolution moiré images.

Reflection removal has been discussed for more than decades. This paper aims to provide the analysis for different reflection properties and factors that influence image formation, an up-to-date taxonomy for existing methods, a benchmark dataset, and the unified benchmarking evaluations for state-of-the-art (especially learning-based) methods. Specifically, this paper presents a SIngle-image Reflection Removal Plus dataset “SIR$^{2+}$2+ ” with the new consideration for in-the-wild scenarios and glass with diverse color and unplanar shapes. We further perform quantitative and visual quality comparisons for state-of-the-art single-image reflection removal algorithms. Open problems for improving reflection removal algorithms are discussed at the end. Our dataset and follow-up update can be found at https://reflectionremoval.github.io/sir2data/.

Along with the ever-growing computational power of mobile devices, mobile visual search has undergone an evolution in techniques and applications. A significant trend is low bit rate visual search, where compact visual descriptors are extracted directly over a mobile and delivered as queries rather than raw images to reduce the query transmission latency. In this article, we introduce our work on low bit rate mobile landmark search, in which a compact yet discriminative landmark image descriptor is extracted by using a location context such as GPS, crowd-sourced hotspot WLAN, and cell tower locations. The compactness originates from the bag-of-words image representation, with offline learning from geotagged photos from online photo-sharing websites including Flickr and Panoramio. The learning process involves segmenting the landmark photo collection by discrete geographical regions using a Gaussian mixture model and then boosting a ranking-sensitive vocabulary within each region, with 'entropy'-based feedback on the compactness of the descriptor to refine both phases iteratively. In online search, when entering a geographical region, the code book in a mobile device is downstream adapted to generate extremely compact descriptors with promising discriminative ability. We have deployed landmark search apps to both HTC and iPhone mobile phones, accessing a database of a million scale images in typical areas like Beijing, New York, and Barcelona, and others. Our descriptor outperforms alternative compact descriptors (Chen et al. 2009; Chen et al., 2010; Chandrasekhar et al. 2009a; Chandrasekhar et al. 2009b) by significant margins. Beyond landmark search, this article will summarize the MPEG standarization progress of compact descriptor for visual search (CDVS) (Yuri et al. 2010; Yuri et al. 2011) toward application interoperability., ********** In recent years, mobile devices, such as smart camera phones and tablet PCs, have shown great potential for visual search, thanks to the integrated functionality of high-resolution embedded cameras, [...]

Various innovative and original works have been applied and proposed in the field of sports video analysis. However, individual works have focused on sophisticated methodologies with particular sport types and there has been a lack of scalable and holistic frameworks in this field. This article proposes a solution and presents a systematic and generic approach which is experimented on a relatively large-scale sports consortia. The system aims at the event detection scenario of an input video with an orderly sequential process. Initially, domain knowledge-independent local descriptors are extracted homogeneously from the input video sequence. Then the video representation is created by adopting a bag-of-visual-words (BoW) model. The video’s genre is first identified by applying the k-nearest neighbor (k-NN) classifiers on the initially obtained video representation, and various dissimilarity measures are assessed and evaluated analytically. Subsequently, an unsupervised probabilistic latent semantic analysis (PLSA)-based approach is employed at the same histogram-based video representation, characterizing each frame of video sequence into one of four view groups, namely closed-up-view, mid-view, long-view, and outer-field-view. Finally, a hidden conditional random field (HCRF) structured prediction model is utilized for interesting event detection. From experimental results, k-NN classifier using KL-divergence measurement demonstrates the best accuracy at 82.16% for genre categorization. Supervised SVM and unsupervised PLSA have average classification accuracies at 82.86% and 68.13%, respectively. The HCRF model achieves 92.31% accuracy using the unsupervised PLSA based label input, which is comparable with the supervised SVM based input at an accuracy of 93.08%. In general, such a systematic approach can be widely applied in processing massive videos generically.

Remote sensing images (RSIs) taken in hazy conditions, such as haze, fog, thin could, snow, silt, dust, offgas, etc., suffer from sever color and contrast degradations. Dehazing algorithm is therefore highly demanded to restore hazed RSIs from their degradations. In the literatures, most dehazing algorithms were originally designed for natural images dehazing (NID). For our analysis, the physical model of NID is different from that of RSI dehazing (RSID), which was not clearly addressed yet. In this paper, a new concept of "virtual depth" concerning physical model of RSI is firstly raised. Virtual depth is different from real depth of nature image in that the former gives the distance of an object departing from the foreground, while the later measures the coverings of the earth's surface, such as snow, dust, cloud and haze/fog. These coverings act as the hazes in a natural image, providing the hint of foreground and background. Secondly, an I terative De hazing for R emote S ensing image (IDeRS) is proposed, in which dehazing operator is implemented iteratively to remove haze progressively until arriving at a satisfied result. In IDeRS, we also raise a fusion model for combining patch-wise and pixel-wise dehazing operators to overcome both halos and over-saturation caused by them respectively. Extensive experimental results tested on publicly available databases demonstrate that the proposed IDeRS outperforms most state-of-the-arts in RSID. [ABSTRACT FROM AUTHOR]