914 research outputs found
A Unified Approach to Restoration, Deinterlacing and Resolution Enhancement in Decoding MPEG-2 Video
Generative Compression
Traditional image and video compression algorithms rely on hand-crafted
encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the
data being compressed. Here we describe the concept of generative compression,
the compression of data using generative models, and suggest that it is a
direction worth pursuing to produce more accurate and visually pleasing
reconstructions at much deeper compression levels for both image and video
data. We also demonstrate that generative compression is orders-of-magnitude
more resilient to bit error rates (e.g. from noisy wireless channels) than
traditional variable-length coding schemes
Algorithms & implementation of advanced video coding standards
Advanced video coding standards have become widely deployed coding techniques used in numerous products, such as broadcast, video conference, mobile television and blu-ray disc, etc. New compression techniques are gradually included in video coding standards so that a 50% compression rate reduction is achievable every five years. However, the trend also has brought many problems, such as, dramatically increased computational complexity, co-existing multiple standards and gradually increased development time. To solve the above problems, this thesis intends to investigate efficient algorithms for the latest video coding standard, H.264/AVC. Two aspects of H.264/AVC standard are inspected in this thesis: (1) Speeding up intra4x4 prediction with parallel architecture. (2) Applying an efficient rate control algorithm based on deviation measure to intra frame. Another aim of this thesis is to work on low-complexity algorithms for MPEG-2 to H.264/AVC transcoder. Three main mapping algorithms and a computational complexity reduction algorithm are focused by this thesis: motion vector mapping, block mapping, field-frame mapping and efficient modes ranking algorithms. Finally, a new video coding framework methodology to reduce development time is examined. This thesis explores the implementation of MPEG-4 simple profile with the RVC framework. A key technique of automatically generating variable length decoder table is solved in this thesis. Moreover, another important video coding standard, DV/DVCPRO, is further modeled by RVC framework. Consequently, besides the available MPEG-4 simple profile and China audio/video standard, a new member is therefore added into the RVC framework family. A part of the research work presented in this thesis is targeted algorithms and implementation of video coding standards. In the wide topic, three main problems are investigated. The results show that the methodologies presented in this thesis are efficient and encourage
Audiovisual preservation strategies, data models and value-chains
This is a report on preservation strategies, models and value-chains for digital file-based audiovisual content. The report includes: (a)current and emerging value-chains and business-models for audiovisual preservation;(b) a comparison of preservation strategies for audiovisual content including their strengths and weaknesses, and(c) a review of current preservation metadata models, and requirements for extension to support audiovisual files
End-to-End Learnable Multi-Scale Feature Compression for VCM
The proliferation of deep learning-based machine vision applications has
given rise to a new type of compression, so called video coding for machine
(VCM). VCM differs from traditional video coding in that it is optimized for
machine vision performance instead of human visual quality. In the feature
compression track of MPEG-VCM, multi-scale features extracted from images are
subject to compression. Recent feature compression works have demonstrated that
the versatile video coding (VVC) standard-based approach can achieve a BD-rate
reduction of up to 96% against MPEG-VCM feature anchor. However, it is still
sub-optimal as VVC was not designed for extracted features but for natural
images. Moreover, the high encoding complexity of VVC makes it difficult to
design a lightweight encoder without sacrificing performance. To address these
challenges, we propose a novel multi-scale feature compression method that
enables both the end-to-end optimization on the extracted features and the
design of lightweight encoders. The proposed model combines a learnable
compressor with a multi-scale feature fusion network so that the redundancy in
the multi-scale features is effectively removed. Instead of simply cascading
the fusion network and the compression network, we integrate the fusion and
encoding processes in an interleaved way. Our model first encodes a
larger-scale feature to obtain a latent representation and then fuses the
latent with a smaller-scale feature. This process is successively performed
until the smallest-scale feature is fused and then the encoded latent at the
final stage is entropy-coded for transmission. The results show that our model
outperforms previous approaches by at least 52% BD-rate reduction and has
to times less encoding time for object detection. It is
noteworthy that our model can attain near-lossless task performance with only
0.002-0.003% of the uncompressed feature data size.Comment: Under peer review for IEEE TCSV
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
Deep-learning based precoding techniques for next-generation video compression
Several research groups worldwide are currently investigating how deep learning may advance the state-of-the-art in image and video coding. An open question is how to make deep neural networks work in conjunction with existing (and upcoming) video codecs, such as MPEG AVC/H.264, HEVC, VVC, Google VP9 and AOMedia AV1, as well as existing container and transport formats. Such compatibility is a crucial aspect, as the video content industry and hardware manufacturers are expected to remain committed to supporting these standards for the foreseeable future. We propose deep neural networks as precoding components for current and future codec ecosystems. In our current deployments for DASH/HLS adaptive streaming, this comprises downscaling neural networks. Precoding via deep learning allows for full compatibility to current and future codec and transport standards while providing for significant savings. Our results with HD content show that 23%-43% rate reduction takes place under a range of state-of-the-art video codec implementations. The use of precoding can also lead to significant encoding complexity reduction, which is essential for the cloud deployment of complex encoders like AV1 and MPEG VVC. Therefore, beyond bitrate saving, deep-learning based precoding may reduce the required cloud resources for video transcoding and make cloud-based solutions competitive or superior to state-of-the-art captive deployments
- …