More and more visual-quality metrics are being developed to assess the quality of images, but little research has considered their limitations. In this paper, we demonstrate that image preprocessing before compression can artificially increase the quality scores provided by the popular metrics DISTS, LPIPS, HaarPSI, and VIF. We propose a series of neural-network preprocessing models that increase DISTS by up to 34.5%, LPIPS by up to 36.8%, VIF by up to 98.0%, and HaarPSI by up to 22.6% in the case of JPEG-compressed images. However, a subjective comparison of these preprocessed images showed that the visual quality either dropped or remained unchanged, indicating the limited applicability of these metrics. We used a ResNet-like lightweight CNN architecture for preprocessing and the differentiable DiffJPEG algorithm for compression.

Modern codecs offer numerous settings that can alter the encoding process. Multiobjective video encoding optimization has been studied in various works, but the whole encoder's option space was never considered. In this paper, we present a method for multiobjective encoding optimization in terms of relative video bitrate and encoding speed over the entire option space for a given encoder.

This paper presents a software-based method for estimating the power consumption of video decoders on various Android devices. Using this method, we developed an automatic system that consists of the VEQE Android application to measure the power consumption of video decoders and a server to collect the metrics. The system allowed us to create power-consumption and decoding-speed dataset for video decoders operating on 236 devices, representing 147 models. The paper presents several charts: the top 30 models and video decoders in terms of power efficiency for playback and for decoding only, as well as video-decoder ratings by power consumption and decoding speed for a given device model.

Modern video codecs have many compression-tuning parameters from which numerous configurations (presets) can be constructed. The large number of presets complicates the search for one that delivers optimal encoding time, quality, and compressed-video size. This paper presents a machine-learning-based method that helps to solve this problem. We applied the method to the x264 video codec: it searches for optimal presets that demonstrate 9-20% bitrate savings relative to standard x264 presets with comparable compressed-video quality and encoding time. Our method is faster upto 10 times than existing solutions.