Spatially adaptive image compression using a tiled deep network
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images. Existing image compression algorithms based on neural networks learn quantized representations with a constant spatial bit rate across each image. While entropy coding introduces some spatial variation, traditional codecs have benefited significantly by explicitly adapting the bit rate based on local image complexity and visual saliency. This paper introduces an algorithm that combines deep neural networks with quality-sensitive bit rate adaptation using a tiled network. We demonstrate the importance of spatial context prediction and show improved quantitative (PSNR) and qualitative (subjective rater assessment) results compared to a non-adaptive baseline and a recently published image compression model based on fully-convolutional neural networks.
Code (0)
등록된 구현이 없습니다.
Tasks
Image CompressionSimilar Papers 제목 키워드 기반
Dual-layer Image Compression via Adaptive Downsampling and Spatially Varying Upconversion
Ultra high resolution (UHR) images are almost always downsampled to fit small displays of mobile end devices and upsampled to its original resolution when exhibited on very high-resolution displays. This observation moti…
DecoderImage CompressionLVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression
Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in p…
Image CompressionQuantizationLearning Content-Weighted Deep Image Compression
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance. Most existing methods adopt spatially invariant bit length allocation and incorporate discrete entropy approx…
DecoderImage CompressionSpatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks
We propose an off-line approach to explicitly encode temporal patterns spatially as different types of images, namely, Gramian Angular Fields and Markov Transition Fields. This enables the use of techniques from computer…
ClassificationGeneral ClassificationTime SeriesTime Series AnalysisFrescoDiffusion: 4K Image-to-Video with Prior-Regularized Tiled Diffusion
Diffusion-based image-to-video (I2V) models are increasingly effective, yet they struggle to scale to ultra-high-resolution inputs (e.g., 4K). Generating videos at the model's native resolution often loses fine-grained s…