4 research outputs found
Convolutional Neural Networks based Intra Prediction for HEVC
Traditional intra prediction methods for HEVC rely on using the nearest
reference lines for predicting a block, which ignore much richer context
between the current block and its neighboring blocks and therefore cause
inaccurate prediction especially when weak spatial correlation exists between
the current block and the reference lines. To overcome this problem, in this
paper, an intra prediction convolutional neural network (IPCNN) is proposed for
intra prediction, which exploits the rich context of the current block and
therefore is capable of improving the accuracy of predicting the current block.
Meanwhile, the predictions of the three nearest blocks can also be refined. To
the best of our knowledge, this is the first paper that directly applies CNNs
to intra prediction for HEVC. Experimental results validate the effectiveness
of applying CNNs to intra prediction and achieved significant performance
improvement compared to traditional intra prediction methods.Comment: 10 pages, This is the extended edition of poster paper accepted by
DCC 201
Efficient Multiple Line-Based Intra Prediction for HEVC
Traditional intra prediction usually utilizes the nearest reference line to
generate the predicted block when considering strong spatial correlation.
However, this kind of single line-based method does not always work well due to
at least two issues. One is the incoherence caused by the signal noise or the
texture of other object, where this texture deviates from the inherent texture
of the current block. The other reason is that the nearest reference line
usually has worse reconstruction quality in block-based video coding. Due to
these two issues, this paper proposes an efficient multiple line-based intra
prediction scheme to improve coding efficiency. Besides the nearest reference
line, further reference lines are also utilized. The further reference lines
with relatively higher quality can provide potential better prediction. At the
same time, the residue compensation is introduced to calibrate the prediction
of boundary regions in a block when we utilize further reference lines. To
speed up the encoding process, this paper designs several fast algorithms.
Experimental results show that, compared with HM-16.9, the proposed fast search
method achieves 2.0% bit saving on average and up to 3.7%, with increasing the
encoding time by 112%.Comment: Accepted for publication in IEEE Transactions on Circuits and Systems
for Video Technolog
Inpainting-based Video Compression in FullHD
Compression methods based on inpainting are an evolving alternative to
classical transform-based codecs for still images. Attempts to apply these
ideas to video compression are rare, since reaching real-time performance is
very challenging. Therefore, current approaches focus on simplified
frame-by-frame reconstructions that ignore temporal redundancies. As a remedy,
we propose a highly efficient, real-time capable prediction and correction
approach that fully relies on partial differential equations (PDEs) in all
steps of the codec: Dense variational optic flow fields yield accurate
motion-compensated predictions, while homogeneous diffusion inpainting is
applied for intra prediction. To compress residuals, we introduce a new highly
efficient block-based variant of pseudodifferential inpainting. Our novel
architecture outperforms other inpainting-based video codecs in terms of both
quality and speed. For the first time in inpainting-based video compression, we
can decompress FullHD (1080p) videos in real-time with a fully CPU-based
implementation, outperforming previous approaches by roughly one order of
magnitude
Enhanced Intra Prediction for Video Coding by Using Multiple Neural Networks
This paper enhances the intra prediction by using multiple neural network
modes (NM). Each NM serves as an end-to-end mapping from the neighboring
reference blocks to the current coding block. For the provided NMs, we present
two schemes (appending and substitution) to integrate the NMs with the
traditional modes (TM) defined in high efficiency video coding (HEVC). For the
appending scheme, each NM is corresponding to a certain range of TMs. The
categorization of TMs is based on the expected prediction errors. After
determining the relevant TMs for each NM, we present a probability-aware mode
signaling scheme. The NMs with higher probabilities to be the best mode are
signaled with fewer bits. For the substitution scheme, we propose to replace
the highest and lowest probable TMs. New most probable mode (MPM) generation
method is also employed when substituting the lowest probable TMs. Experimental
results demonstrate that using multiple NMs will improve the coding efficiency
apparently compared with the single NM. Specifically, proposed appending scheme
with seven NMs can save 2.6%, 3.8%, 3.1% BD-rate for Y, U, V components
compared with using single NM in the state-of-the-art works.Comment: Accepted to IEEE Transactions on Multimedi