Image compression via multiple sampling-rate downsampling and super-resolution upconversion

Abstract

An image compression method utilizing multiple sampling-rate downsampling and super-resolution upconversion is proposed. The multiple sampling-rate downsampling modes and different quantization patterns are designed for each 32 × 32 macroblock at the encoder side. Meanwhile, the rate distortion optimization strategy is investigated to obtain the optimal downsampling and quantization mode. The chosen mode is used to downsample and code the original macroblocks. To obtain the full resolution block, the deep learning-based multiple models super-resolution upconversion is designed to reconstruct the decoded block at the decoder side. The experimental results demonstrate that our method can obtain the higher quality compressed image than JPEG and some state-of-the-art downsampling-based methods at almost all bit rates. At the same decoded image quality, our method can achieve 30% to 55% bit savings at low bit rates and 15% to 30% bit savings at medium to high bit rates. In addition, the proposed framework is applicable to other image compression standards.