Efficient Video Coding based on Audio-Visual Focus of Attention

This paper proposes an efficient video coding method using audio-visual focus of attention, which is based on the observation that sound-emitting regions in an audio-visual sequence draw viewers' attention. First, an audio-visual source localization algorithm is presented, where the sound source is identified by using the correlation between the sound signal and the visual motion information. The localization result is then used to encode different regions in the scene with different quality in such a way that regions close to the source are encoded with higher quality than those far from the source. This is implemented in the framework of H.264/AVC by assigning different quantization parameters for different regions. Through experiments with both standard and high definition sequences, it is demonstrated that the proposed method can yield considerable coding gains over the constant quantization mode of H.264/AVC without noticeable degradation of perceived quality

Audiovisual focus of attention and its application to Ultra High Definition video compression

Using Focus of Attention (FoA) as a perceptual process in image and video compression belongs to well-known approaches to increase coding efficiency. It has been shown that foveated coding, when compression quality varies across the image according to region of interest, is more efficient than the alternative coding, when all region are compressed in a similar way. However, widespread use of such foveated compression has been prevented due to two main conflicting causes, namely, the complexity and the efficiency of algorithms for FoA detection. One way around these is to use as much information as possible from the scene. Since most video sequences have an associated audio, and moreover, in many cases there is a correlation between the audio and the visual content, audiovisual FoA can improve efficiency of the detection algorithm while remaining of low complexity. This paper discusses a simple yet efficient audiovisual FoA algorithm based on correlation of dynamics between audio and video signal components. Results of audiovisual FoA detection algorithm are subsequently taken into account for foveated coding and compression. This approach is implemented into H.265/HEVC encoder producing a bitstream which is fully compliant to any H.265/HEVC decoder. The influence of audiovisual FoA in the perceived quality of high and ultra-high definition audiovisual sequences is explored and the amount of gain in compression efficiency is analyzed

Mobile Audiovisual Terminal: System Design and Subjective Testing in DECT and UMTS networks

It is anticipated that there will shortly be a requirement for multimedia terminals that operate via mobile communications systems. This paper presents a functional specification for such a terminal operating at 32 kb/s in a digital European cordless telecommunications (DECT) and universal mobile telecommunications system (UMTS) radio network. A terminal has been built, based on a PC with digital signal processor (DSP) boards for audio and video coding and decoding. Speech coding is by a phonetically driven code-excited linear prediction (CELP) speech coder and video coding by a block-oriented hybrid discrete cosine transform (DCT) coder. Separate channel coding is provided for the audio and video data. The paper describes the techniques used for audio and video coding, channel coding, and synchronization. Methods of subjective testing in a DECT network and in a UMTS network are also described. These consisted of subjective tests of first impressions of the mobile audio–visual terminal (MAVT) quality, interactive tests, and the completion of an exit questionnaire. The test results showed that the quality of the audio was sufficiently good for comprehension and the video was sufficiently good for following and repeating simple mechanical tasks. However, the quality of the MAVT was not good enough for general use where high-quality audio and video was needed, especially when transmission was in a noisy radio environment

Multimodal Sparse Coding for Event Detection

Unsupervised feature learning methods have proven effective for classification tasks based on a single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities. The shared representations are applied to multimedia event detection (MED) and evaluated in comparison to unimodal counterparts, as well as other feature learning methods such as GMM supervectors and sparse RBM. We report the cross-validated classification accuracy and mean average precision of the MED system trained on features learned from our unimodal and multimodal settings for a subset of the TRECVID MED 2014 dataset.Comment: Multimodal Machine Learning Workshop at NIPS 201

Taking video cameras into the classroom.

Research into the communication and interactions in classrooms need to take the multimodal nature of classrooms into account. Video cameras can capture the dynamics of teaching and learning, but the use of videos for research purposes needs to be well thought through in order to accommodate the challenges this tool holds. This article refers to three research projects where videos were used to generate data. It is argued that videos allow the researcher to hone in on the micro-details and, in contrast to other data generation tools, allows researchers who were not present at the time to view what has been witnessed. A video recording is a data source but not data by itself and the information that is discerned from a video is framed and shaped by the research paradigm and the questions asked

Fusion of Learned Multi-Modal Representations and Dense Trajectories for Emotional Analysis in Videos

When designing a video affective content analysis algorithm, one of the most important steps is the selection of discriminative features for the effective representation of video segments. The majority of existing affective content analysis methods either use low-level audio-visual features or generate handcrafted higher level representations based on these low-level features. We propose in this work to use deep learning methods, in particular convolutional neural networks (CNNs), in order to automatically learn and extract mid-level representations from raw data. To this end, we exploit the audio and visual modality of videos by employing Mel-Frequency Cepstral Coefficients (MFCC) and color values in the HSV color space. We also incorporate dense trajectory based motion features in order to further enhance the performance of the analysis. By means of multi-class support vector machines (SVMs) and fusion mechanisms, music video clips are classified into one of four affective categories representing the four quadrants of the Valence-Arousal (VA) space. Results obtained on a subset of the DEAP dataset show (1) that higher level representations perform better than low-level features, and (2) that incorporating motion information leads to a notable performance gain, independently from the chosen representation