Enhancing performance of conventional image codecs using CNN based image sub-sampling and super resolution
Bibliographic record
Abstract
The goal of image compression is to reduce the number of bits required to represent an image with a minimum loss of visual quality. However, conventional image compression algorithms, such as JPEG, produce unpleasant artifacts in decoded images at high compression ratios. This thesis investigates a CNN-based approach to improve the performance of the widely used JPEG codec. In recent times, there has been an increasing interest in using convolutional neural network (CNN) for various image processing tasks, owing to their ability to learn very compact features from images. However, the use of CNNs to improve the performance of existing image codecs is very limited in literature. Motivated by this, we investigate a CNN based image compression framework which improves the performance of the JPEG algorithm by optimally sub-sampling the input image with a CNN referred to as compact convolutional neural network (ComCNN) prior to JPEG encoding and by performing super resolution and enhancement of the decoded image with a CNN referred to as enhancement based reconstruction convolutional neural network (EBR-CNN). Both CNNs are optimally trained to minimize the end-to-end image distortion for a given value of the JPEG quality factor. Experimental results are presented which compare the performance of the proposed compression framework with several alternative learning and non-learning based image sub-sampling and super resolution methods. These results show that the proposed method provides noticeable improvements in decoded image quality compared to the other alternatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".