6 research outputs found
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent,
convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000,
and JPEG as measured by MS-SSIM. We introduce three improvements over previous
research that lead to this state-of-the-art result. First, we show that
training with a pixel-wise loss weighted by SSIM increases reconstruction
quality according to several metrics. Second, we modify the recurrent
architecture to improve spatial diffusion, which allows the network to more
effectively capture and propagate image information through the network's
hidden state. Finally, in addition to lossless entropy coding, we use a
spatially adaptive bit allocation algorithm to more efficiently use the limited
number of bits to encode visually complex image regions. We evaluate our method
on the Kodak and Tecnick image sets and compare against standard codecs as well
recently published methods based on deep neural networks
Towards a Semantic Perceptual Image Metric
We present a full reference, perceptual image metric based on VGG-16, an
artificial neural network trained on object classification. We fit the metric
to a new database based on 140k unique images annotated with ground truth by
human raters who received minimal instruction. The resulting metric shows
competitive performance on TID 2013, a database widely used to assess image
quality assessments methods. More interestingly, it shows strong responses to
objects potentially carrying semantic relevance such as faces and text, which
we demonstrate using a visualization technique and ablation experiments. In
effect, the metric appears to model a higher influence of semantic context on
judgments, which we observe particularly in untrained raters. As the vast
majority of users of image processing systems are unfamiliar with Image Quality
Assessment (IQA) tasks, these findings may have significant impact on
real-world applications of perceptual metrics