Importance weighted compression
The connection between variational autoencoders (VAEs) and compression is well established and they have been used for both lossless and lossy compression. Compared to VAEs, importance-weighted autoencoders (IWAEs) achieve a larger bound on the log-likelihood. However, it is not well understood whether a similar connection between IWAEs and compression exists and whether the improved loss corresponds to better compression performance. Here we show that the loss of IWAEs can indeed be interpreted as the cost of lossless or lossy compression schemes, and using IWAEs for compression can lead to small improvements in performance.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning 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 CompressionNon-Uniform Quantisation for 3DGS Compression
3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, yet its high bitrate requirements pose significant challenges for storage and transmission. To enable practical applications and …
Novel View SynthesisLearning Convolutional Networks for Content-weighted Image Compression
Lossy image compression is generally formulated as a joint rate-distortion optimization to learn encoder, quantizer, and decoder. However, the quantizer is non-differentiable, and discrete entropy estimation usually is r…
BinarizationDecoderImage CompressionSSIMDipSVD: Dual-importance Protected SVD for Efficient LLM Compression
The ever-increasing computational demands and deployment costs of large language models (LLMs) have spurred numerous compressing methods. Compared to quantization and unstructured pruning, SVD compression offers superior…
Model CompressionQuantizationA GAN-based Tunable Image Compression System
The method of importance map has been widely adopted in DNN-based lossy image compression to achieve bit allocation according to the importance of image contents. However, insufficient allocation of bits in non-important…
Generative Adversarial NetworkImage CompressionMS-SSIMSSIM