H.265/HEVC Decoding via Iterative Recovery From Incomplete Quantized Measurements

This letter is dedicated to improving the quality of video sequences compressed by the H.265/HEVC standard. We propose to consider this problem as a signal recovery from incomplete measurements taken in the HEVC transform domain. The recovery could be obtained via <inline-formula><tex-math...

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Bibliographic Details
Published in:IEEE signal processing letters Vol. 32; pp. 4149 - 4153
Main Authors: Mahfod, Karam, Belyaev, Evgeny
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Binary sequences
Coding
compressive sensing
Decoding
Encoding
Filtering
Formability
HEVC
Image reconstruction
Information retrieval
Neural networks
Operators (mathematics)
Pixels
Recovery
Sensors
Signal reconstruction
Signal to noise ratio
Silicon
Transforms
Video compression
video enhancement
Video sequences
Videos
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary:This letter is dedicated to improving the quality of video sequences compressed by the H.265/HEVC standard. We propose to consider this problem as a signal recovery from incomplete measurements taken in the HEVC transform domain. The recovery could be obtained via <inline-formula><tex-math notation="LaTeX">l_{1}</tex-math></inline-formula>-minimization using the Iterative Shrinkage-Thresholding Algorithm (ISTA) well known in compressive sensing (CS) framework. However, in case of HEVC the ISTA updating step cannot be performed directly via matrix multiplications, since the sensing is performed using high-complex hybrid intra- and motion-compensated prediction in pixel domain, and the frame sensing matrix depends on the current and corresponding reference frames along with the encoder compression profile. In order to overcome these limitations, in this letter, we first propose to modify the HEVC decoder so that it also obtains the prediction values for each pixel taking into account all the coding modes within the input bitstream. Second, we propose to modify the ISTA updating step by introducing encoding and decoding operators applied instead of the matrix multiplication by sensing matrix and its transpose, respectively. These operators use the obtained prediction values, as well as prediction mode, motion vectors, quantization step, and transform type extracted for each coding unit from the input bitstream in order to replicate the encoding and the decoding process except the entropy coding. Herewith, the ISTA thresholding stage is performed by an image or video enhancement neural network. Experimental results show that the proposed approach provides up to 1 dB improvement in Peak Signal-to-Noise Ratio (PSNR) compared to the state-of-the-art approaches such as Recursive Fusion and Deformable Spatiotemporal Attention (RFDA), Spatio-Temporal Detail Information Retrieval (STDR) and Coding Priors-Guided Aggregation (CPGA).
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2025.3624678