Abstract: According to investigating impact of complexity of
stereoscopic frame pairs on stereoscopic video coding and
transmission, a new rate control algorithm is presented. The proposed
rate control algorithm is performed on three levels: stereoscopic group
of pictures (SGOP) level, stereoscopic frame (SFrame) level and
frame level. A temporal-spatial frame complexity model is firstly
established, in the bits allocation stage, the frame complexity, position
significance and reference property between the left and right frames
are taken into account. Meanwhile, the target buffer is set according to
the frame complexity. Experimental results show that the proposed
method can efficiently control the bitrates, and it outperforms the fixed
quantization parameter method from the rate distortion perspective,
and average PSNR gain between rate-distortion curves (BDPSNR) is
0.21dB.
Abstract: In this paper, low end Digital Signal Processors (DSPs)
are applied to accelerate integer neural networks. The use of DSPs
to accelerate neural networks has been a topic of study for some
time, and has demonstrated significant performance improvements.
Recently, work has been done on integer only neural networks, which
greatly reduces hardware requirements, and thus allows for cheaper
hardware implementation. DSPs with Arithmetic Logic Units (ALUs)
that support floating or fixed point arithmetic are generally more
expensive than their integer only counterparts due to increased circuit
complexity. However if the need for floating or fixed point math
operation can be removed, then simpler, lower cost DSPs can be
used. To achieve this, an integer only neural network is created in
this paper, which is then accelerated by using DSP instructions to
improve performance.