Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor

We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams. Our approach begins with the observation that all modern video codecs divide the input frames into macroblocks (MBs). We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a.k.a., MB texture decoding). This in turn allows for the derivation of spatio-temporal video activity regions at extremely high speed in comparison to conventional full-frame decoding followed by optical flow estimation. In order to evaluate the accuracy of a video classification framework based on such activity data, we independently train two CNN architectures on MB texture and MV correspondences and then fuse their scores to derive the final classification of each test video. Evaluation on two standard datasets shows that the proposed approach is competitive to the best two-stream video classification approaches found in the literature. At the same time: (i) a CPU-based realization of our MV extraction is over 977 times faster than GPU-based optical flow methods; (ii) selective decoding is up to 12 times faster than full-frame decoding; (iii) our proposed spatial and temporal CNNs perform inference at 5 to 49 times lower cloud computing cost than the fastest methods from the literature.Comment: Accepted in IEEE Transactions on Circuits and Systems for Video Technology. Extension of ICIP 2017 conference pape

Study of Compression Statistics and Prediction of Rate-Distortion Curves for Video Texture

Encoding textural content remains a challenge for current standardised video codecs. It is therefore beneficial to understand video textures in terms of both their spatio-temporal characteristics and their encoding statistics in order to optimize encoding performance. In this paper, we analyse the spatio-temporal features and statistics of video textures, explore the rate-quality performance of different texture types and investigate models to mathematically describe them. For all considered theoretical models, we employ machine-learning regression to predict the rate-quality curves based solely on selected spatio-temporal features extracted from uncompressed content. All experiments were performed on homogeneous video textures to ensure validity of the observations. The results of the regression indicate that using an exponential model we can more accurately predict the expected rate-quality curve (with a mean Bj{\o}ntegaard Delta rate of 0.46% over the considered dataset) while maintaining a low relative complexity. This is expected to be adopted by in the loop processes for faster encoding decisions such as rate-distortion optimisation, adaptive quantization, partitioning, etc.Comment: 17 page

Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application

Survey of Error Concealment techniques: Research directions and open issues

© 2015 IEEE. Error Concealment (EC) techniques use either spatial, temporal or a combination of both types of information to recover the data lost in transmitted video. In this paper, existing EC techniques are reviewed, which are divided into three categories, namely Intra-frame EC, Inter-frame EC, and Hybrid EC techniques. We first focus on the EC techniques developed for the H.264/AVC standard. The advantages and disadvantages of these EC techniques are summarized with respect to the features in H.264. Then, the EC algorithms are also analyzed. These EC algorithms have been recently adopted in the newly introduced H.265/HEVC standard. A performance comparison between the classic EC techniques developed for H.264 and H.265 is performed in terms of the average PSNR. Lastly, open issues in the EC domain are addressed for future research consideration

Adapting Computer Vision Models To Limitations On Input Dimensionality And Model Complexity

When considering instances of distributed systems where visual sensors communicate with remote predictive models, data traffic is limited to the capacity of communication channels, and hardware limits the processing of collected data prior to transmission. We study novel methods of adapting visual inference to limitations on complexity and data availability at test time, wherever the aforementioned limitations exist. Our contributions detailed in this thesis consider both task-specific and task-generic approaches to reducing the data requirement for inference, and evaluate our proposed methods on a wide range of computer vision tasks. This thesis makes four distinct contributions: (i) We investigate multi-class action classification via two-stream convolutional neural networks that directly ingest information extracted from compressed video bitstreams. We show that selective access to macroblock motion vector information provides a good low-dimensional approximation of the underlying optical flow in visual sequences. (ii) We devise a bitstream cropping method by which AVC/H.264 and H.265 bitstreams are reduced to the minimum amount of necessary elements for optical flow extraction, while maintaining compliance with codec standards. We additionally study the effect of codec rate-quality control on the sparsity and noise incurred on optical flow derived from resulting bitstreams, and do so for multiple coding standards. (iii) We demonstrate degrees of variability in the amount of data required for action classification, and leverage this to reduce the dimensionality of input volumes by inferring the required temporal extent for accurate classification prior to processing via learnable machines. (iv) We extend the Mixtures-of-Experts (MoE) paradigm to adapt the data cost of inference for any set of constituent experts. We postulate that the minimum acceptable data cost of inference varies for different input space partitions, and consider mixtures where each expert is designed to meet a different set of constraints on input dimensionality. To take advantage of the flexibility of such mixtures in processing different input representations and modalities, we train biased gating functions such that experts requiring less information to make their inferences are favoured to others. We finally note that, our proposed data utility optimization solutions include a learnable component which considers specified priorities on the amount of information to be used prior to inference, and can be realized for any combination of tasks, modalities, and constraints on available data