Benchmarking Neural Image Compression with DCT-Based Spectral Diagnostics

Abstract

Neural image compression (NIC) models are commonly evaluated with scalar rate-distortion metrics, which do not show how a codec treats individual spatial frequencies. We propose a benchmark that probes codecs with the DCT basis matrix and single-frequency test images. We measure relative off-target energy (leakage), the fraction of reconstruction energy outside the probed frequency, together with target-frequency gain. Exact decompositions and bounds relate these measurements to reconstruction error. At operating points nearest 1 bpp, the BMSHJ, MBT, and Cheng2020 models leave 80-97% of their reconstructed high-band energy outside the target bin and have target-frequency gains of 0.02-0.23, whereas TCM and FTIC attain off-target energy in the classical-codec range. Both probe types identify behavior absent from scalar metrics: released Cheng2020-Anchor models produce extreme pre-clamp outputs on isolated single-frequency inputs. Finally, decoder-only fine-tuning with natural-image and leakage losses reduces basis off-target energy by 90-99.8% across all eight evaluated architectures while keeping the bitstream fixed, at a Kodak PSNR cost of 0.09-0.59 dB.

arXiv comment17 pages, including supplementary material. Code: https://github.com/nkalmykovsk/dct_benchmark_nic