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hello everyone I'm Chen Fong a PhD student at visual information lab from University of Bristol I like to present our paper rank dqa dpqa based on ranking hybrid training the work was created together with my colleague D Daniel and our supervisors Dr F and Professor David B here is a quick review about R dqa they proposed a new two-stage training methodology using ranking inspired the L functions we developed a large scale V training database with reliable quality labels results performing cost delay subjective test we trained a Transformer based Network for patch level vqa and L employed multiple small video databases to train an aggregation Network for SP tempor pooling to obtain sequence level quality indices this figure shows the performance of the proposed metrics and selected Benchmark V method both full reference and no reference rank achieve the best performance in each category in recent years deep learning has driven the development of video C assessment methods achieving prisent results compared to the conventional approaches as it show the high level framework deep Vu normally extracts and process the features of the video patches and the L obtain the sequence level Quality Index through the agregation of the patch level quality indices such as deep vqa lpip and the C3 dqa however DQ methods tend to be limited by the following issues the problem one is the L of reliable large and diverse training databases the most existing method was trained using a relatively small video database typically is only a few hundred subjective labels because the subjective test is time and costly consuming this is insufficient for training a model with a relatively High network capacity this is why most existing deep V masses only perform well when cross validated on a single database and the show and satisfactory cross data sites generalization ability the problem two is ineffective Training Method the existing training process or design only consider one distorted content each time meanwhile the methods just minimize the distance between the patch level and the sequence level quality indices from the ground truth and in contrast the ability of a metric to differentiate the quality of differently distorted versions is an important charistics of the vqa method that has been not yet been exploited named the ranking ability it address the problem one we propose a new two-state training methodology this first steps uses VMA based quality ranking information to train deep Rec model this enables us to develop a large scale training data site for optimizing patch level VQ results performing costly subjective tests the maap was used due to its consistent correlation with subjective ground truth and the L employs multiple small video quality databases to train agregation Network for spio temporal pooling by aing this new training framework the proposed deep V method can achieve significantly improved mod generalization and the W need to perform in database across validation for the problem two we utilizing the ranking information rather than Absolut values of the vmf or subjective scores during the training process we take two distorted sequences each time let's re formalize vqa as differentiating the quality between the two disted videos it makes it meaningful to use v as training Target allowing using a large training database ranking experence training laws the use of multiple subjective databases for drawn training with different viewing conditions and the score scaling the architecture of rank is shown here based on the new training methodology we have trained the full reference and no reference dpq method employing a Transformer based architecture for stage one and 3D based agregation Network for stage two I explained each stage of list in next few slid in the stage one we developed a Transformer based pqa KN for patch level assessment the input includes a distorted and reference patches PD and PR with resolution 256x 256 in three channels and two frames the output is qu a index of L distorted patch we employ a paramet network for feature extraction and a spu temper module for Quality estimation here the multiple scale quality waiting ing to the extract features for the training material and the strategy of the stage one we take 230 sources video from the BB DVC and click we employs for most popular video Codec with four different conation level and four resolution adaptations this results a total of 14,720 this toed sequences during the training progress we take two pairs of the patches to Output the product quality indices Q patch a at Q patch B we first calculate a probability P using the product quality indices Q patch a q patch B with a sing mode function we progress the probability p and the vmf difference after banalization vmf B by Banner cross entropy loss as the stage one loss function in stage two we designed a spot temporal aggregation Network St KN which accepted both patch level score tensors and the distorted feature Maps extracted from stage one to obtain the final sequence level quality score these Ted feature maps are used to weight the patch level quality indices in stage two we employ a similar ranking inspired training strategy rather than simply minimizing the difference between product quality and the subject score which allowing multiple subjective quality databases for drawn training the training databases are M plus and ivp due to their various video content it takes two videos each time in the training progress the input is two setes of quality indices and the feature Maps generated by stage one target is normalize the subjective score SX and s y a total of 16 s sequences Paris from the both databases and the loss of stn KN is ranking inpiring shown here to evaluate the performance of proposed method eight video quality data sites were employed result intro database cross validation both full reference and no reference rank are tested for no reference reference patches are not included as input we tested 10 full reference and six no reference called in Matrix as benchmarks shown here the results in the table shows the full reference rank achieves the highest overall St value of the 0. 8972 across eight databases the second best performer is V MAF based on the F test results the performance differences is significance on the V bvhd and mclv databases in terms of complexity the runtime of the full reference RQ is 3.88 times lower than the B MAF similar to The underperforming Deep full reference vqa method deep VQ QA in the new reference case rank dqa also offers the best overall performance with an average stroke of 0.77 91 this figure is much higher than the lot of the second best quality metric gstv QA Str equ to 0.704 we also conducted appliation study on ranking inspired Training Method and the training framework from the results of the appliation study each of this made contribution to the final result there are some visual examples for the same Source frames on the left the distorted video a is given a higher subjective quality score than video B and the rank correctly protects less similarity for the different sources frames on the right although distorted video B shows the less visual Distortion from its reference compared to the distorted between the distorted video a and reference a we fails to predict the correct quality ranking the proposed me the do in this work we propose a new two-stage training methology for deep V ranking inspired the L functions we introduce a large scale training database with reliable quality labels to optimize PCH level deep vqa we train agregation Network for spal tempal pooling to obtain sequence level quality indices to the best of knowledge the proposed full reference quality metric is the first that consistently outperforms we Ma on various video quality databases is result R training the proposed method full reference and no reference rank achieves day of art VQ performance future work should focus on more certificated NW work architectures for SP to Temp pooling and complexity reduction for multiple lowlevel version tasks I really want to thanks my colleague Mr do Daniel and appreciate my supervisors Dr fan Jong and Professor David bu many thanks here is references thank you